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How to Write Recommendations in Research | Examples & Tips

Published on September 15, 2022 by Tegan George . Revised on July 18, 2023.

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyze the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic. Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

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Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, other interesting articles, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

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There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar subjects to your paper or dissertation topic . They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Important evidence or analysis that wasn’t mentioned in the discussion section and results section
• Generic concluding phrases (e.g. “In conclusion …”)
• Weak statements that undermine your argument (e.g., “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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What are Implications and Recommendations in Research? How to Write It, with Examples

Highly cited research articles often contain both implications and recommendations , but there is often some confusion around the difference between implications and recommendations in research. Implications of a study are the impact your research makes in your chosen area; they discuss how the findings of the study may be important to justify further exploration of your research topic. Research recommendations suggest future actions or subsequent steps supported by your research findings. It helps to improve your field of research or cross-disciplinary fields through future research or provides frameworks for decision-makers or policymakers. Recommendations are the action plan you propose based on the outcome.

In this article, we aim to simplify these concepts for researchers by providing key insights on the following:  

• what are implications in research 
• what is recommendation in research 
• differences between implications and recommendations 
• how to write implications in research 
• how to write recommendation in research 
• sample recommendation in research 

Table of Contents

What are implications in research

The implications in research explain what the findings of the study mean to researchers or to certain subgroups or populations beyond the basic interpretation of results. Even if your findings fail to bring radical or disruptive changes to existing ways of doing things, they might have important implications for future research studies. For example, your proposed method for operating remote-controlled robots could be more precise, efficient, or cheaper than existing methods, or the remote-controlled robot could be used in other application areas. This could enable more researchers to study a specific problem or open up new research opportunities.   

Implications in research inform how the findings, drawn from your results, may be important for and impact policy, practice, theory, and subsequent research. Implications may be theoretical or practical. 1  

• Practical implications are potential values of the study with practical or real outcomes . Determining the practical implications of several solutions can aid in identifying optimal solution results. For example, clinical research or research on classroom learning mostly has practical implications in research . If you developed a new teaching method, the implication would be how teachers can use that method based on your findings.  
• Theoretical implications in research constitute additions to existing theories or establish new theories. These types of implications in research characterize the ability of research to influence society in apparent ways. It is, at most, an educated guess (theoretical) about the possible implication of action and need not be as absolute as practical implications in research . If your study supported the tested theory, the theoretical implication would be that the theory can explain the investigated phenomenon. Else, your study may serve as a basis for modifying the theory. Theories may be partially supported as well, implying further study of the theory or necessary modifications are required.  

What are recommendations in research?

Recommendations in research can be considered an important segment of the analysis phase. Recommendations allow you to suggest specific interventions or strategies to address the issues and constraints identified through your study. It responds to key findings arrived at through data collection and analysis. A process of prioritization can help you narrow down important findings for which recommendations are developed.  

Recommendations in research examples

Recommendations in research may vary depending on the purpose or beneficiary as seen in the table below.  

Table: Recommendations in research examples based on purpose and beneficiary  

If you’re wondering how to make recommendations in research . You can use the simple  recommendation in research example below as a handy template.  

Table: Sample recommendation in research template  

Basic differences between implications and recommendations in research

Implications and recommendations in research are two important aspects of a research paper or your thesis or dissertation. Implications discuss the importance of the research findings, while recommendations offer specific actions to solve a problem. So, the basic difference between the two is in their function and the questions asked to achieve it. The following table highlights the main differences between implications and recommendations in research .  

Table: Differences between implications and recommendations in research  

Where do implications go in your research paper

Because the implications and recommendations of the research are based on study findings, both are usually written after the completion of a study. There is no specific section dedicated to implications in research ; they are usually integrated into the discussion section adding evidence as to why the results are meaningful and what they add to the field. Implications can be written after summarizing your main findings and before the recommendations and conclusion.   

Implications can also be presented in the conclusion section after a short summary of the study results.   

How to write implications in research

Implication means something that is inferred. The implications of your research are derived from the importance of your work and how it will impact future research. It is based on how previous studies have advanced your field and how your study can add to that.   

When figuring out how to write implications in research , a good strategy is to separate it into the different types of implications in research , such as social, political, technological, policy-related, or others. As mentioned earlier, the most frequently used are the theoretical and practical implications.   

Next, you need to ask, “Who will benefit the most from reading my paper?” Is it policymakers, physicians, the public, or other researchers? Once you know your target population, explain how your findings can help them.  

The implication section can include a paragraph or two that asserts the practical or managerial implications and links it to the study findings. A discussion can then follow, demonstrating that the findings can be practically implemented or how they will benefit a specific audience. The writer is given a specific degree of freedom when writing research implications , depending on the type of implication in research you want to discuss: practical or theoretical. Each is discussed differently, using different words or in separate sections. The implications can be based on how the findings in your study are similar or dissimilar to that in previous studies. Your study may reaffirm or disprove the results of other studies, which has important implications in research . You can also suggest future research directions in the light of your findings or require further research to confirm your findings, which are all crucial implications. Most importantly, ensure the implications in research are specific and that your tone reflects the strength of your findings without exaggerating your results.   

Implications in research can begin with the following specific sentence structures:  

• These findings suggest that…
• These results build on existing body of evidence of…
• These results should be considered when…
• While previous research focused on x, our results show that y…

What should recommendations in research look like?

Recommendations for future research should be:  

• Directly related to your research question or findings  
• Concrete and specific  
• Supported by a clear reasoning  

The recommendations in research can be based on the following factors:  

1. Beneficiary: A paper’s research contribution may be aimed at single or multiple beneficiaries, based on which recommendations can vary. For instance, if your research is about the quality of care in hospitals, the research recommendation to different beneficiaries might be as follows:  

• Nursing staff: Staff should undergo training to enhance their understanding of what quality of care entails.  
• Health science educators: Educators must design training modules that address quality-related issues in the hospital.  
• Hospital management: Develop policies that will increase staff participation in training related to health science.  

2. Limitations: The best way to figure out what to include in your research recommendations is to understand the limitations of your study. It could be based on factors that you have overlooked or could not consider in your present study. Accordingly, the researcher can recommend that other researchers approach the problem from a different perspective, dimension, or methodology. For example, research into the quality of care in hospitals can be based on quantitative data. The researcher can then recommend a qualitative study of factors influencing the quality of care, or they can suggest investigating the problem from the perspective of patients rather than the healthcare providers.   

3. Theory or Practice: Your recommendations in research could be implementation-oriented or further research-oriented.   

4. Your research: Research recommendations can be based on your topic, research objectives, literature review, and analysis, or evidence collected. For example, if your data points to the role of faculty involvement in developing effective programs, recommendations in research can include developing policies to increase faculty participation. Take a look at the evidence-based recommendation in research example s provided below.   

Table: Example of evidence-based research recommendation  

Avoid making the following mistakes when writing research recommendations :  

• Don’t undermine your own work: Recommendations in research should offer suggestions on how future studies can be built upon the current study as a natural extension of your work and not as an entirely new field of research.  
• Support your study arguments: Ensure that your research findings stand alone on their own merits to showcase the strength of your research paper.   

How to write recommendations in research

When writing research recommendations , your focus should be on highlighting what additional work can be done in that field. It gives direction to researchers, industries, or governments about changes or developments possible in this field. For example, recommendations in research can include practical and obtainable strategies offering suggestions to academia to address problems. It can also be a framework that helps government agencies in developing strategic or long-term plans for timely actions against disasters or aid nation-building.  

There are a few SMART 2 things to remember when writing recommendations in research. Your recommendations must be: 

• S pecific: Clearly state how challenges can be addressed for better outcomes and include an action plan that shows what can be achieved. 
• M easurable: Use verbs denoting measurable outcomes, such as identify, analyze, design, compute, assess, evaluate, revise, plan, etc., to strengthen recommendations in research .   
• A ttainable: Recommendations should offer a solution-oriented approach to problem-solving and must be written in a way that is easy to follow.  
• R elevant: Research recommendations should be reasonable, realistic, and result-based. Make sure to suggest future possibilities for your research field.  
• T imely: Time-based or time-sensitive recommendations in research help divide the action plan into long-term or short-term (immediate) goals. A timeline can also inform potential readers of what developments should occur over time.  

If you are wondering how many words to include in your research recommendation , a general rule of thumb would be to set aside 5% of the total word count for writing research recommendations . Finally, when writing the research implications and recommendations , stick to the facts and avoid overstating or over-generalizing the study findings. Both should be supported by evidence gathered through your data analysis.  

References:  

• Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings.  Psychological bulletin ,  124 (2), 262.
• Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives.  Manag Rev ,  70 (11), 35-36.

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The Ultimate Guide to Crafting Impactful Recommendations in Research

Are you ready to take your research to the next level? Crafting impactful recommendations is the key to unlocking the full potential of your study. By providing clear, actionable suggestions based on your findings, you can bridge the gap between research and real-world application.

In this ultimate guide, we'll show you how to write recommendations that make a difference in your research report or paper.

You'll learn how to craft specific, actionable recommendations that connect seamlessly with your research findings. Whether you're a student, writer, teacher, or journalist, this guide will help you master the art of writing recommendations in research. Let's get started and make your research count!

Understanding the Purpose of Recommendations

Recommendations in research serve as a vital bridge between your findings and their real-world applications. They provide specific, action-oriented suggestions to guide future studies and decision-making processes. Let's dive into the key purposes of crafting effective recommendations:

Guiding Future Research

Research recommendations play a crucial role in steering scholars and researchers towards promising avenues of exploration. By highlighting gaps in current knowledge and proposing new research questions, recommendations help advance the field and drive innovation.

Influencing Decision-Making

Well-crafted recommendations have the power to shape policies, programs, and strategies across various domains, such as:

• Policy-making
• Product development
• Marketing strategies
• Medical practice

By providing clear, evidence-based suggestions, recommendations facilitate informed decision-making and improve outcomes.

Connecting Research to Practice

Recommendations act as a conduit for transferring knowledge from researchers to practitioners, policymakers, and stakeholders. They bridge the gap between academic findings and their practical applications, ensuring that research insights are effectively translated into real-world solutions.

Enhancing Research Impact

By crafting impactful recommendations, you can amplify the reach and influence of your research, attracting attention from peers, funding agencies, and decision-makers.

Addressing Limitations

Recommendations provide an opportunity to acknowledge and address the limitations of your study. By suggesting concrete and actionable possibilities for future research, you demonstrate a thorough understanding of your work's scope and potential areas for improvement.

Identifying Areas for Future Research

Discovering research gaps is a crucial step in crafting impactful recommendations. It involves reviewing existing studies and identifying unanswered questions or problems that warrant further investigation. Here are some strategies to help you identify areas for future research:

Explore Research Limitations

Take a close look at the limitations section of relevant studies. These limitations often provide valuable insights into potential areas for future research. Consider how addressing these limitations could enhance our understanding of the topic at hand.

Critically Analyze Discussion and Future Research Sections

When reading articles, pay special attention to the discussion and future research sections. These sections often highlight gaps in the current knowledge base and propose avenues for further exploration. Take note of any recurring themes or unanswered questions that emerge across multiple studies.

Utilize Targeted Search Terms

To streamline your search for research gaps, use targeted search terms such as "literature gap" or "future research" in combination with your subject keywords. This approach can help you quickly identify articles that explicitly discuss areas for future investigation.

Seek Guidance from Experts

Don't hesitate to reach out to your research advisor or other experts in your field. Their wealth of knowledge and experience can provide valuable insights into potential research gaps and emerging trends.

By employing these strategies, you'll be well-equipped to identify research gaps and craft recommendations that push the boundaries of current knowledge. Remember, the goal is to refine your research questions and focus your efforts on areas where more understanding is needed.

Structuring Your Recommendations

When it comes to structuring your recommendations, it's essential to keep them concise, organized, and tailored to your audience. Here are some key tips to help you craft impactful recommendations:

Prioritize and Organize

• Limit your recommendations to the most relevant and targeted suggestions for your peers or colleagues in the field.
• Place your recommendations at the end of the report, as they are often top of mind for readers.
• Write your recommendations in order of priority, with the most important ones for decision-makers coming first.

Use a Clear and Actionable Format

• Write recommendations in a clear, concise manner using actionable words derived from the data analyzed in your research.
• Use bullet points instead of long paragraphs for clarity and readability.
• Ensure that your recommendations are specific, measurable, attainable, relevant, and timely (SMART).

Connect Recommendations to Research

By following this simple formula, you can ensure that your recommendations are directly connected to your research and supported by a clear rationale.

Tailor to Your Audience

• Consider the needs and interests of your target audience when crafting your recommendations.
• Explain how your recommendations can solve the issues explored in your research.
• Acknowledge any limitations or constraints of your study that may impact the implementation of your recommendations.

Avoid Common Pitfalls

• Don't undermine your own work by suggesting incomplete or unnecessary recommendations.
• Avoid using recommendations as a place for self-criticism or introducing new information not covered in your research.
• Ensure that your recommendations are achievable and comprehensive, offering practical solutions for the issues considered in your paper.

By structuring your recommendations effectively, you can enhance the reliability and validity of your research findings, provide valuable strategies and suggestions for future research, and deliver impactful solutions to real-world problems.

Crafting Actionable and Specific Recommendations

Crafting actionable and specific recommendations is the key to ensuring your research findings have a real-world impact. Here are some essential tips to keep in mind:

Embrace Flexibility and Feasibility

Your recommendations should be open to discussion and new information, rather than being set in stone. Consider the following:

• Be realistic and considerate of your team's capabilities when making recommendations.
• Prioritize recommendations based on impact and reach, but be prepared to adjust based on team effort levels.
• Focus on solutions that require the fewest changes first, adopting an MVP (Minimum Viable Product) approach.

Provide Detailed and Justified Recommendations

To avoid vagueness and misinterpretation, ensure your recommendations are:

• Detailed, including photos, videos, or screenshots whenever possible.
• Justified based on research findings, providing alternatives when findings don't align with expectations or business goals.

Use this formula when writing recommendations:

Observed problem/pain point/unmet need + consequence + potential solution

Adopt a Solution-Oriented Approach

Foster Collaboration and Participation

• Promote staff education on current research and create strategies to encourage adoption of promising clinical protocols.
• Include representatives from the treatment community in the development of the research initiative and the review of proposals.
• Require active, early, and permanent participation of treatment staff in the development, implementation, and interpretation of the study.

Tailor Recommendations to the Opportunity

When writing recommendations for a specific opportunity or program:

• Highlight the strengths and qualifications of the researcher.
• Provide specific examples of their work and accomplishments.
• Explain how their research has contributed to the field.
• Emphasize the researcher's potential for future success and their unique contributions.

By following these guidelines, you'll craft actionable and specific recommendations that drive meaningful change and showcase the value of your research.

Connecting Recommendations with Research Findings

Connecting your recommendations with research findings is crucial for ensuring the credibility and impact of your suggestions. Here's how you can seamlessly link your recommendations to the evidence uncovered in your study:

Grounding Recommendations in Research

Your recommendations should be firmly rooted in the data and insights gathered during your research process. Avoid including measures or suggestions that were not discussed or supported by your study findings. This approach ensures that your recommendations are evidence-based and directly relevant to the research at hand.

Highlighting the Significance of Collaboration

Research collaborations offer a wealth of benefits that can enhance an agency's competitive position. Consider the following factors when discussing the importance of collaboration in your recommendations:

• Organizational Development: Participation in research collaborations depends on an agency's stage of development, compatibility with its mission and culture, and financial stability.
• Trust-Building: Long-term collaboration success often hinges on a history of increasing involvement and trust between partners.
• Infrastructure: A permanent infrastructure that facilitates long-term development is key to successful collaborative programs.

Emphasizing Commitment and Participation

Fostering Quality Improvement and Organizational Learning

In your recommendations, highlight the importance of enhancing quality improvement strategies and fostering organizational learning. Show sensitivity to the needs and constraints of community-based programs, as this understanding is crucial for effective collaboration and implementation.

Addressing Limitations and Implications

If not already addressed in the discussion section, your recommendations should mention the limitations of the study and their implications. Examples of limitations include:

• Sample size or composition
• Participant attrition
• Study duration

By acknowledging these limitations, you demonstrate a comprehensive understanding of your research and its potential impact.

By connecting your recommendations with research findings, you provide a solid foundation for your suggestions, emphasize the significance of collaboration, and showcase the potential for future research and practical applications.

Crafting impactful recommendations is a vital skill for any researcher looking to bridge the gap between their findings and real-world applications. By understanding the purpose of recommendations, identifying areas for future research, structuring your suggestions effectively, and connecting them to your research findings, you can unlock the full potential of your study. Remember to prioritize actionable, specific, and evidence-based recommendations that foster collaboration and drive meaningful change.

As you embark on your research journey, embrace the power of well-crafted recommendations to amplify the impact of your work. By following the guidelines outlined in this ultimate guide, you'll be well-equipped to write recommendations that resonate with your audience, inspire further investigation, and contribute to the advancement of your field. So go forth, make your research count, and let your recommendations be the catalyst for positive change.

Q: What are the steps to formulating recommendations in research? A: To formulate recommendations in research, you should first gain a thorough understanding of the research question. Review the existing literature to inform your recommendations and consider the research methods that were used. Identify which data collection techniques were employed and propose suitable data analysis methods. It's also essential to consider any limitations and ethical considerations of your research. Justify your recommendations clearly and finally, provide a summary of your recommendations.

Q: Why are recommendations significant in research studies? A: Recommendations play a crucial role in research as they form a key part of the analysis phase. They provide specific suggestions for interventions or strategies that address the problems and limitations discovered during the study. Recommendations are a direct response to the main findings derived from data collection and analysis, and they can guide future actions or research.

Q: Can you outline the seven steps involved in writing a research paper? A: Certainly. The seven steps to writing an excellent research paper include:

• Allowing yourself sufficient time to complete the paper.
• Defining the scope of your essay and crafting a clear thesis statement.
• Conducting a thorough yet focused search for relevant research materials.
• Reading the research materials carefully and taking detailed notes.
• Writing your paper based on the information you've gathered and analyzed.
• Editing your paper to ensure clarity, coherence, and correctness.
• Submitting your paper following the guidelines provided.

Q: What tips can help make a research paper more effective? A: To enhance the effectiveness of a research paper, plan for the extensive process ahead and understand your audience. Decide on the structure your research writing will take and describe your methodology clearly. Write in a straightforward and clear manner, avoiding the use of clichés or overly complex language.

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Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of exploration. In an era marked by rapid technological advancements and an ever-expanding knowledge base, refining the process of generating research recommendations becomes imperative.

But, what is a research recommendation?

Research recommendations are suggestions or advice provided to researchers to guide their study on a specific topic . They are typically given by experts in the field. Research recommendations are more action-oriented and provide specific guidance for decision-makers, unlike implications that are broader and focus on the broader significance and consequences of the research findings. However, both are crucial components of a research study.

Difference Between Research Recommendations and Implication

Although research recommendations and implications are distinct components of a research study, they are closely related. The differences between them are as follows:

Types of Research Recommendations

Recommendations in research can take various forms, which are as follows:

These recommendations aim to assist researchers in navigating the vast landscape of academic knowledge.

Let us dive deeper to know about its key components and the steps to write an impactful research recommendation.

Key Components of Research Recommendations

The key components of research recommendations include defining the research question or objective, specifying research methods, outlining data collection and analysis processes, presenting results and conclusions, addressing limitations, and suggesting areas for future research. Here are some characteristics of research recommendations:

Research recommendations offer various advantages and play a crucial role in ensuring that research findings contribute to positive outcomes in various fields. However, they also have few limitations which highlights the significance of a well-crafted research recommendation in offering the promised advantages.

The importance of research recommendations ranges in various fields, influencing policy-making, program development, product development, marketing strategies, medical practice, and scientific research. Their purpose is to transfer knowledge from researchers to practitioners, policymakers, or stakeholders, facilitating informed decision-making and improving outcomes in different domains.

How to Write Research Recommendations?

Research recommendations can be generated through various means, including algorithmic approaches, expert opinions, or collaborative filtering techniques. Here is a step-wise guide to build your understanding on the development of research recommendations.

1. Understand the Research Question:

Understand the research question and objectives before writing recommendations. Also, ensure that your recommendations are relevant and directly address the goals of the study.

2. Review Existing Literature:

Familiarize yourself with relevant existing literature to help you identify gaps , and offer informed recommendations that contribute to the existing body of research.

3. Consider Research Methods:

Evaluate the appropriateness of different research methods in addressing the research question. Also, consider the nature of the data, the study design, and the specific objectives.

4. Identify Data Collection Techniques:

Gather dataset from diverse authentic sources. Include information such as keywords, abstracts, authors, publication dates, and citation metrics to provide a rich foundation for analysis.

5. Propose Data Analysis Methods:

Suggest appropriate data analysis methods based on the type of data collected. Consider whether statistical analysis, qualitative analysis, or a mixed-methods approach is most suitable.

6. Consider Limitations and Ethical Considerations:

Acknowledge any limitations and potential ethical considerations of the study. Furthermore, address these limitations or mitigate ethical concerns to ensure responsible research.

7. Justify Recommendations:

Explain how your recommendation contributes to addressing the research question or objective. Provide a strong rationale to help researchers understand the importance of following your suggestions.

8. Summarize Recommendations:

Provide a concise summary at the end of the report to emphasize how following these recommendations will contribute to the overall success of the research project.

By following these steps, you can create research recommendations that are actionable and contribute meaningfully to the success of the research project.

Download now to unlock some tips to improve your journey of writing research recommendations.

Example of a Research Recommendation

Here is an example of a research recommendation based on a hypothetical research to improve your understanding.

Research Recommendation: Enhancing Student Learning through Integrated Learning Platforms

Background:

The research study investigated the impact of an integrated learning platform on student learning outcomes in high school mathematics classes. The findings revealed a statistically significant improvement in student performance and engagement when compared to traditional teaching methods.

Recommendation:

In light of the research findings, it is recommended that educational institutions consider adopting and integrating the identified learning platform into their mathematics curriculum. The following specific recommendations are provided:

• Implementation of the Integrated Learning Platform:

Schools are encouraged to adopt the integrated learning platform in mathematics classrooms, ensuring proper training for teachers on its effective utilization.

• Professional Development for Educators:

Develop and implement professional programs to train educators in the effective use of the integrated learning platform to address any challenges teachers may face during the transition.

• Monitoring and Evaluation:

Establish a monitoring and evaluation system to track the impact of the integrated learning platform on student performance over time.

• Resource Allocation:

Allocate sufficient resources, both financial and technical, to support the widespread implementation of the integrated learning platform.

By implementing these recommendations, educational institutions can harness the potential of the integrated learning platform and enhance student learning experiences and academic achievements in mathematics.

This example covers the components of a research recommendation, providing specific actions based on the research findings, identifying the target audience, and outlining practical steps for implementation.

Using AI in Research Recommendation Writing

Enhancing research recommendations is an ongoing endeavor that requires the integration of cutting-edge technologies, collaborative efforts, and ethical considerations. By embracing data-driven approaches and leveraging advanced technologies, the research community can create more effective and personalized recommendation systems. However, it is accompanied by several limitations. Therefore, it is essential to approach the use of AI in research with a critical mindset, and complement its capabilities with human expertise and judgment.

Here are some limitations of integrating AI in writing research recommendation and some ways on how to counter them.

1. Data Bias

AI systems rely heavily on data for training. If the training data is biased or incomplete, the AI model may produce biased results or recommendations.

How to tackle: Audit regularly the model’s performance to identify any discrepancies and adjust the training data and algorithms accordingly.

2. Lack of Understanding of Context:

AI models may struggle to understand the nuanced context of a particular research problem. They may misinterpret information, leading to inaccurate recommendations.

How to tackle: Use AI to characterize research articles and topics. Employ them to extract features like keywords, authorship patterns and content-based details.

3. Ethical Considerations:

AI models might stereotype certain concepts or generate recommendations that could have negative consequences for certain individuals or groups.

How to tackle: Incorporate user feedback mechanisms to reduce redundancies. Establish an ethics review process for AI models in research recommendation writing.

4. Lack of Creativity and Intuition:

AI may struggle with tasks that require a deep understanding of the underlying principles or the ability to think outside the box.

How to tackle: Hybrid approaches can be employed by integrating AI in data analysis and identifying patterns for accelerating the data interpretation process.

5. Interpretability:

Many AI models, especially complex deep learning models, lack transparency on how the model arrived at a particular recommendation.

How to tackle: Implement models like decision trees or linear models. Provide clear explanation of the model architecture, training process, and decision-making criteria.

6. Dynamic Nature of Research:

Research fields are dynamic, and new information is constantly emerging. AI models may struggle to keep up with the rapidly changing landscape and may not be able to adapt to new developments.

How to tackle: Establish a feedback loop for continuous improvement. Regularly update the recommendation system based on user feedback and emerging research trends.

The integration of AI in research recommendation writing holds great promise for advancing knowledge and streamlining the research process. However, navigating these concerns is pivotal in ensuring the responsible deployment of these technologies. Researchers need to understand the use of responsible use of AI in research and must be aware of the ethical considerations.

Exploring research recommendations plays a critical role in shaping the trajectory of scientific inquiry. It serves as a compass, guiding researchers toward more robust methodologies, collaborative endeavors, and innovative approaches. Embracing these suggestions not only enhances the quality of individual studies but also contributes to the collective advancement of human understanding.

Frequently Asked Questions

The purpose of recommendations in research is to provide practical and actionable suggestions based on the study's findings, guiding future actions, policies, or interventions in a specific field or context. Recommendations bridges the gap between research outcomes and their real-world application.

To make a research recommendation, analyze your findings, identify key insights, and propose specific, evidence-based actions. Include the relevance of the recommendations to the study's objectives and provide practical steps for implementation.

Begin a recommendation by succinctly summarizing the key findings of the research. Clearly state the purpose of the recommendation and its intended impact. Use a direct and actionable language to convey the suggested course of action.

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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How To Write Recommendations In A Research Study

Published by Alvin Nicolas at July 12th, 2024 , Revised On July 12, 2024

The ultimate goal of any research process is not just to gather knowledge, but to use that knowledge to make a positive impact. This is where recommendations come in.  A well-written recommendations section in your research study translates your findings into actionable steps and guides future research on the topic. 

This blog is your ultimate guide to understanding how to write recommendations in a research study. But before that, let’s see what is recommendation in research. 

What Is Recommendation In Research 

In a research study, the recommendation section refers to a suggested course of action based on the findings of your research . It acts as a bridge between the knowledge you gained and its practical implications. 

Recommendations take your research results and propose concrete steps on how to use them to address a problem or improve a situation. Moreover, you can suggest new avenues and guide future research in building upon your work. This will improve the credibility of your research. For studies that include real-world implications, recommendations are a great way to provide evidence-based suggestions for policymakers or practitioners to consider. 

Difference Between Research Recommendations and Implication

Research recommendations and implications often confuse researchers. They cannot easily differentiate between the two. Here is how they are different. 

Where To Add Recommendations 

Recommendations are mostly part of your conclusion and discussion sections. If you are writing a practical dissertation , you can include a separate section for your recommendations. 

Types of Research Recommendations

There are different forms of recommendations in research. Some of them include the following. 

How To Construct The Recommendations Section

There are different ways in which different scholars write the recommendations section. A general observation is a research question → conclusion → recommendation.

The following example will help you understand this better.

Research Question

How can the education of mothers impact the social skills of kindergarten children?

The role of mothers is a significant contributor towards the social skills of children. From an early age, kids tend to observe how their mother interacts with others and follow in her footsteps initially. Therefore, mothers should be educated and interact with good demeanour if they want their children to have excellent social skills.

Recommendation

The study revealed that a mother’s education plays an important role in building the social skills of children on kindergarten level. Future research could explore how the same continues in junior school level children.

How To Write Recommendations In Research

Now that you are familiar with the definition and types, here is a step-by-step guide on how to write a recommendation in research.

Step 1: Revisit Your Research Goals

Before doing anything else, you have to remind yourself of the objectives that you set out to achieve in your research. It allows you to match your recommendations directly to your research questions and see if you made any contribution to your goals.

Step 2: Analyse Your Findings

You have to examine your data and identify your key results. This analysis forms the foundation for your recommendations. Look for patterns and unexpected findings that might suggest new areas for other researchers to explore.

Step 3: Consider The Research Methods

Ask these questions from yourself: were the research methods effective? Is there any other way that would have been better to perform this research, or were there any limitations associated with the research methods?

Step 4: Prioritise Recommendations

You might have a lot of recommendations in mind, but all are not equal. You have to consider the impact and feasibility of each suggestion. Prioritise these recommendations, while remaining realistic about implementation.

Step 5: Write Actionable Statements

Do not be vague when crafting statements. Instead, you have to use clear and concise language that outlines specific actions. For example, if you want to say “improve education practices,” you could write “implement a teacher training program” for better clarity.

Step 6: Provide Evidence

You cannot just make suggestions out of thin air, and have to ground them in the evidence you have gathered through your research. Moreover, cite relevant data or findings from your study or previous literature to support your recommendations.

Step 7: Address Challenges

There are always some limitations related to the research at hand. As a researcher, it is your duty to highlight and address any challenges faced or what might occur in the future.

Tips For Writing The Perfect Recommendation In Research

Use these tips to write the perfect recommendation in your research.

• Be Concise – Write recommendations in a clear and concise language. Use one sentence statements to look more professional.
• Be Logical & Coherent – You can use lists and headings according to the requirements of your university.
• Tailor According To Your Readers – You have to aim your recommendations to a specific audience and colleagues in the field of study.
• Provide Specific Suggestions – Offer specific measures and solutions to the issues, and focus on actionable suggestions.
• Match Recommendations To Your Conclusion – You have to align your recommendations with your conclusion.
• Consider Limitations – Use critical thinking to see how limitations may impact the feasibility of your solutions.
• End With A Summary – You have to add a small conclusion to highlight suggestions and their impact.

Example Of Recommendation In Research

Context of the study:

This research studies how effective e-learning platforms are for adult language learners compared to traditional classroom instruction. The findings suggest that e-learning platforms can be just as effective as traditional classrooms in improving language proficiency.

Research Recommendation Sample

Language educators can incorporate e-learning tools into existing curriculums to provide learners with more flexibility. Additionally, they can develop training programs for educators on how to integrate e-learning platforms into their teaching practices.

E-learning platform developers should focus on e-learning platforms that are interactive and cater to different learning styles. They can also invest in features that promote learner autonomy and self-directed learning.

Future researchers can further explore the long-term effects of e-learning on language acquisition to provide insights into whether e-learning can support sustained language development.

Frequently Asked Questions

How to write recommendations in a research paper.

• Revisit your research goals
• Analyse your findings 
• Consider the research methods 
• Prioritise recommendations 
• Write actionable statements 
• Provide evidence 
• Address challenges

How to present recommendations in research?

• Be concise 
• Write logical and coherent 
• Match recommendations to conclusion 
• Ensure your recommendations are achievable

What to write in recommendation in research?

Your recommendation has to be concrete and specific and support the research with a clear rationale. Moreover, it should be connected directly to your research. Your recommendations, however, should not undermine your own work or use self-criticism. 

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Implications or Recommendations in Research: What's the Difference?

• Peer Review

High-quality research articles that get many citations contain both implications and recommendations. Implications are the impact your research makes, whereas recommendations are specific actions that can then be taken based on your findings, such as for more research or for policymaking.

Updated on August 23, 2022

That seems clear enough, but the two are commonly confused.

This confusion is especially true if you come from a so-called high-context culture in which information is often implied based on the situation, as in many Asian cultures. High-context cultures are different from low-context cultures where information is more direct and explicit (as in North America and many European cultures).

Let's set these two straight in a low-context way; i.e., we'll be specific and direct! This is the best way to be in English academic writing because you're writing for the world.

Implications and recommendations in a research article

The standard format of STEM research articles is what's called IMRaD:

• Introduction
• Discussion/conclusions

Some journals call for a separate conclusions section, while others have the conclusions as the last part of the discussion. You'll write these four (or five) sections in the same sequence, though, no matter the journal.

The discussion section is typically where you restate your results and how well they confirmed your hypotheses. Give readers the answer to the questions for which they're looking to you for an answer.

At this point, many researchers assume their paper is finished. After all, aren't the results the most important part? As you might have guessed, no, you're not quite done yet.

The discussion/conclusions section is where to say what happened and what should now happen

The discussion/conclusions section of every good scientific article should contain the implications and recommendations.

The implications, first of all, are the impact your results have on your specific field. A high-impact, highly cited article will also broaden the scope here and provide implications to other fields. This is what makes research cross-disciplinary.

Recommendations, however, are suggestions to improve your field based on your results.

These two aspects help the reader understand your broader content: How and why your work is important to the world. They also tell the reader what can be changed in the future based on your results.

These aspects are what editors are looking for when selecting papers for peer review.

Implications and recommendations are, thus, written at the end of the discussion section, and before the concluding paragraph. They help to “wrap up” your paper. Once your reader understands what you found, the next logical step is what those results mean and what should come next.

Then they can take the baton, in the form of your work, and run with it. That gets you cited and extends your impact!

The order of implications and recommendations also matters. Both are written after you've summarized your main findings in the discussion section. Then, those results are interpreted based on ongoing work in the field. After this, the implications are stated, followed by the recommendations.

Writing an academic research paper is a bit like running a race. Finish strong, with your most important conclusion (recommendation) at the end. Leave readers with an understanding of your work's importance. Avoid generic, obvious phrases like "more research is needed to fully address this issue." Be specific.

The main differences between implications and recommendations (table)

Now let's dig a bit deeper into actually how to write these parts.

What are implications?

Research implications tell us how and why your results are important for the field at large. They help answer the question of “what does it mean?” Implications tell us how your work contributes to your field and what it adds to it. They're used when you want to tell your peers why your research is important for ongoing theory, practice, policymaking, and for future research.

Crucially, your implications must be evidence-based. This means they must be derived from the results in the paper.

Implications are written after you've summarized your main findings in the discussion section. They come before the recommendations and before the concluding paragraph. There is no specific section dedicated to implications. They must be integrated into your discussion so that the reader understands why the results are meaningful and what they add to the field.

A good strategy is to separate your implications into types. Implications can be social, political, technological, related to policies, or others, depending on your topic. The most frequently used types are theoretical and practical. Theoretical implications relate to how your findings connect to other theories or ideas in your field, while practical implications are related to what we can do with the results.

Key features of implications

• State the impact your research makes
• Helps us understand why your results are important
• Must be evidence-based
• Written in the discussion, before recommendations
• Can be theoretical, practical, or other (social, political, etc.)

Examples of implications

Let's take a look at some examples of research results below with their implications.

The result : one study found that learning items over time improves memory more than cramming material in a bunch of information at once .

The implications : This result suggests memory is better when studying is spread out over time, which could be due to memory consolidation processes.

The result : an intervention study found that mindfulness helps improve mental health if you have anxiety.

The implications : This result has implications for the role of executive functions on anxiety.

The result : a study found that musical learning helps language learning in children .

The implications : these findings suggest that language and music may work together to aid development.

What are recommendations?

As noted above, explaining how your results contribute to the real world is an important part of a successful article.

Likewise, stating how your findings can be used to improve something in future research is equally important. This brings us to the recommendations.

Research recommendations are suggestions and solutions you give for certain situations based on your results. Once the reader understands what your results mean with the implications, the next question they need to know is "what's next?"

Recommendations are calls to action on ways certain things in the field can be improved in the future based on your results. Recommendations are used when you want to convey that something different should be done based on what your analyses revealed.

Similar to implications, recommendations are also evidence-based. This means that your recommendations to the field must be drawn directly from your results.

The goal of the recommendations is to make clear, specific, and realistic suggestions to future researchers before they conduct a similar experiment. No matter what area your research is in, there will always be further research to do. Try to think about what would be helpful for other researchers to know before starting their work.

Recommendations are also written in the discussion section. They come after the implications and before the concluding paragraphs. Similar to the implications, there is usually no specific section dedicated to the recommendations. However, depending on how many solutions you want to suggest to the field, they may be written as a subsection.

Key features of recommendations

• Statements about what can be done differently in the field based on your findings
• Must be realistic and specific
• Written in the discussion, after implications and before conclusions
• Related to both your field and, preferably, a wider context to the research

Examples of recommendations

Here are some research results and their recommendations.

A meta-analysis found that actively recalling material from your memory is better than simply re-reading it .

• The recommendation: Based on these findings, teachers and other educators should encourage students to practice active recall strategies.

A medical intervention found that daily exercise helps prevent cardiovascular disease .

• The recommendation: Based on these results, physicians are recommended to encourage patients to exercise and walk regularly. Also recommended is to encourage more walking through public health offices in communities.

A study found that many research articles do not contain the sample sizes needed to statistically confirm their findings .

The recommendation: To improve the current state of the field, researchers should consider doing power analysis based on their experiment's design.

What else is important about implications and recommendations?

When writing recommendations and implications, be careful not to overstate the impact of your results. It can be tempting for researchers to inflate the importance of their findings and make grandiose statements about what their work means.

Remember that implications and recommendations must be coming directly from your results. Therefore, they must be straightforward, realistic, and plausible.

Another good thing to remember is to make sure the implications and recommendations are stated clearly and separately. Do not attach them to the endings of other paragraphs just to add them in. Use similar example phrases as those listed in the table when starting your sentences to clearly indicate when it's an implication and when it's a recommendation.

When your peers, or brand-new readers, read your paper, they shouldn't have to hunt through your discussion to find the implications and recommendations. They should be clear, visible, and understandable on their own.

That'll get you cited more, and you'll make a greater contribution to your area of science while extending the life and impact of your work.

The AJE Team

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How to formulate research recommendations

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• Peer review
• Polly Brown ([email protected]) , publishing manager 1 ,
• Klara Brunnhuber , clinical editor 1 ,
• Kalipso Chalkidou , associate director, research and development 2 ,
• Iain Chalmers , director 3 ,
• Mike Clarke , director 4 ,
• Mark Fenton , editor 3 ,
• Carol Forbes , reviews manager 5 ,
• Julie Glanville , associate director/information service manager 5 ,
• Nicholas J Hicks , consultant in public health medicine 6 ,
• Janet Moody , identification and prioritisation manager 6 ,
• Sara Twaddle , director 7 ,
• Hazim Timimi , systems developer 8 ,
• Pamela Young , senior programme manager 6
• 1 BMJ Publishing Group, London WC1H 9JR,
• 2 National Institute for Health and Clinical Excellence, London WC1V 6NA,
• 3 Database of Uncertainties about the Effects of Treatments, James Lind Alliance Secretariat, James Lind Initiative, Oxford OX2 7LG,
• 4 UK Cochrane Centre, Oxford OX2 7LG,
• 5 Centre for Reviews and Dissemination, University of York, York YO10 5DD,
• 6 National Coordinating Centre for Health Technology Assessment, University of Southampton, Southampton SO16 7PX,
• 7 Scottish Intercollegiate Guidelines Network, Edinburgh EH2 1EN,
• 8 Update Software, Oxford OX2 7LG
• Correspondence to: PBrown
• Accepted 22 September 2006

“More research is needed” is a conclusion that fits most systematic reviews. But authors need to be more specific about what exactly is required

Long awaited reports of new research, systematic reviews, and clinical guidelines are too often a disappointing anticlimax for those wishing to use them to direct future research. After many months or years of effort and intellectual energy put into these projects, authors miss the opportunity to identify unanswered questions and outstanding gaps in the evidence. Most reports contain only a less than helpful, general research recommendation. This means that the potential value of these recommendations is lost.

Current recommendations

In 2005, representatives of organisations commissioning and summarising research, including the BMJ Publishing Group, the Centre for Reviews and Dissemination, the National Coordinating Centre for Health Technology Assessment, the National Institute for Health and Clinical Excellence, the Scottish Intercollegiate Guidelines Network, and the UK Cochrane Centre, met as members of the development group for the Database of Uncertainties about the Effects of Treatments (see bmj.com for details on all participating organisations). Our aim was to discuss the state of research recommendations within our organisations and to develop guidelines for improving the presentation of proposals for further research. All organisations had found weaknesses in the way researchers and authors of systematic reviews and clinical guidelines stated the need for further research. As part of the project, a member of the Centre for Reviews and Dissemination under-took a rapid literature search to identify information on research recommendation models, which found some individual methods but no group initiatives to attempt to standardise recommendations.

Suggested format for research recommendations on the effects of treatments

Core elements.

E Evidence (What is the current state of the evidence?)

P Population (What is …

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What Makes a Good Recommendation?

Characterization of Scientific Paper Recommendations

• Conference paper
• First Online: 02 September 2016
• Cite this conference paper

• Laura Steinert 17 &
• H. Ulrich Hoppe 17  

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9848))

Included in the following conference series:

• CYTED-RITOS International Workshop on Groupware

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In this paper we propose several new measures to characterize sets of scientific papers that provide an overview of a scientific topic. We present a study in which experts were asked to name such papers for one of their areas of expertise and apply the measures to characterize the paper selections. The results are compared to the measured values for random paper selections. We find that the expert selected sets of papers can be characterized to have a moderately high diversity, moderately high coverage and each paper in the set has on average a high prototypicality.

This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under grant No. GRK 2167, Research Training Group “User-Centred Social Media”.

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Scientific paper recommendation systems: a literature review of recent publications

Obtainable from https://aminer.org/citation , dataset V7, last seen on March 31 2016.

Bonchi, F., Pous, D.: Checking NFA equivalence with bisimulations up to congruence. In: ACM SIGPLAN Notices. vol. 48, pp. 457–468. ACM (2013).

http://steinert.collide.info/expertstudy2016.html .

Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr., 1–34 (2015)

Google Scholar  

Dunne, C., Shneiderman, B., Gove, R., Klavans, J., Dorr, B.: Rapid understanding of scientific paper collections: integrating statistics, text analytics, and visualization. J. Am. Soc. Inf. Sci. Technol. 63 (12), 2351–2369 (2012)

Article   Google Scholar  

Jones, N.: User perceived qualities and acceptance of recommender systems. Dissertation, Ecole Polytechnique Federale de Lausanne (2010). http://infoscience.epfl.ch/record/146784

Küçüktunç, O., Saule, E., Kaya, K., Çatalyürek, Ü.V.: Result diversification in automatic citation recommendation. In: Proceedings of the iConference Workshop on Computational Scientometrics: Theory and Applications, pp. 1–4 (2013)

Steinert, L., Chounta, I.-A., Hoppe, H.U.: Where to begin? using network analytics for the recommendation of scientific papers. In: Baloian, N., Zorian, Y., Taslakian, P., Shoukouryan, S. (eds.) CRIWG 2015. LNCS, vol. 9334, pp. 124–139. Springer, Heidelberg (2015)

Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)

Tong, H., He, J., Wen, Z., Konuru, R., Lin, C.Y.: Diversified ranking on large graphs: an optimization viewpoint. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1028–1036. ACM (2011)

Vellino, A.: A comparison between usage-based and citation-based methods for recommending scholarly research articles. Proc. Am. Soc. Inf. Sci. Technol. 47 (1), 1–2 (2010)

Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32. ACM (2005)

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Steinert, L., Hoppe, H.U. (2016). What Makes a Good Recommendation?. In: Yuizono, T., Ogata, H., Hoppe, U., Vassileva, J. (eds) Collaboration and Technology. CRIWG 2016. Lecture Notes in Computer Science(), vol 9848. Springer, Cham. https://doi.org/10.1007/978-3-319-44799-5_9

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The foundation of NICE guidance is the synthesis of evidence primarily through the process of systematic reviewing and, if appropriate, modelling and cost effectiveness decision analysis. The results of these analyses are then discussed by independent committees. These committees include NHS staff, healthcare professionals, social care practitioners, commissioners and providers of care, patients, service users and carers, industry and academics. Stakeholders have the opportunity to comment on draft recommendations before they are finalised. Not only does this process explicitly describe the evidence base, it also identifies where there are gaps, uncertainties or conflicts in the existing evidence.

Many of these uncertainties, although interesting to resolve, are unlikely to affect people’s care or NICE’s ability to produce guidance. However, if these uncertainties may have an effect on NICE’s recommendations it is important for NICE to liaise with the research community to ensure they are addressed. NICE does this by making recommendations for research, which are communicated to researchers and funders. At the time guidance is issued, NICE’s staff and committees have a thorough understanding of the current evidence and valuable insights into uncertainties that need to be resolved. It is important that these are capitalised on.

To undertake its national role effectively, NICE needs to ensure that:

the process of developing the research recommendations is robust, transparent and involves stakeholders

we identify research priorities

we make all research recommendations clearly identifiable in the guidance

the research recommendations provide the information necessary to support research commissioning

the research recommendations are available to researchers and funders by promoting them (for example through the research recommendations database)

the research recommendations are relevant to current practice

we communicate well with the research community.

This process and methods guide has been developed to help guidance-producing centres make research recommendations. It describes a step-by-step approach to identifying uncertainties, formulating research recommendations and research questions, prioritising them and communicating them to the NICE Science Policy and Research (SP&R) team, researchers and funders. It has been developed based on the SP&R team’s interactions with research funders and researchers, as well as with guidance developers.

Keywords: research gaps; uncertainties; research recommendations; NICE Process and Methods Guides.

Copyright © 2015 National Institute for Health and Clinical Excellence, unless otherwise stated. All rights reserved.

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Article Contents

What does it mean for a recommendation to be evidence-based.

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Robert L Schmidt, Rachel E Factor, What Does it Mean for a Recommendation to be Evidence-Based?, Laboratory Medicine , Volume 50, Issue 1, February 2019, Pages 5–7, https://doi.org/10.1093/labmed/lmy071

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Clinical laboratories are under increasing pressure to demonstrate value. 1 Traditionally, value has been defined in terms of performance measures that are under the direct control of the laboratory, such as analytical performance, cost-efficiency, and operational performance (eg, turnaround time). However, laboratories can have a significant influence over the entire diagnostic process, from preanalytical test selection to postanalytical test interpretation, all of which can have a significant impact on value. For that reason, there is increasing scrutiny on the role of the laboratory role in the diagnostic process.

Recent studies have shown that there is wide variation in diagnostic practice, which is potentially harmful. This variation can be minimized if laboratories follow evidence-based recommendations. 2 , 3 , 4

What do we mean by evidence-based recommendations and how do we arrive at them? The term evidence-based refers to a decision process following a theoretic framework that determines relevant outcomes, prioritizes their importance, estimates the probability that each outcome will occur, estimates the costs and benefits of each outcome, evaluates whether the benefits of an intervention outweigh its harms and, finally, uses this information as a rationale for 1 or more recommendation(s) regarding the intervention. All the steps in the process should be systematic and transparent so that they can be evaluated.

Evidence-based decision making is not strictly algorithmic and recognizes that each situation is unique. Evidence-based recommendations provide a starting point that can be modified by the clinical context. Thus, evidence-based decisions must incorporate patient preferences and clinical judgment.

There are many systems for producing evidence-based recommendations, which has led to confusion about the meaning of the term evidence-based . Recently, the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group developed a systematic and explicit approach for developing evidence-based guidelines 5 that has been widely adopted. Our discussion will focus on the GRADE approach.

Although tests are used to provide information for a wide range of purposes (eg, diagnosis, prognosis, screening, and monitoring), the GRADE process is flexible and can be applied to developing recommendations for all of these purposes. Also, the GRADE process is applicable to all medical interventions and is not limited to clinical laboratory tests.

The first step in the process is to formulate the problem, usually as a question, and identify the important outcomes. 6 Clinical problems can be described using the PICO (population, intervention, comparison, outcome) framework modified for testing (population, index test, comparator or reference test, and outcome). This step is critical because it focuses the task and determines the type of evidence that is required.

The second step is to gather evidence. Data gathering should be systematic, comprehensive, and reproducible, following the well-established procedures for a systematic review.

The next step evaluates the quality of the evidence, which often depends on study type. Filtered evidence (systematic reviews and meta-analysis) is generally considered to be of higher-quality than unfiltered evidence (individual studies). Among individual studies, controlled studies are considered to contribute stronger evidence than observational studies which, in turn, are considered stronger than expert opinion, case studies, and narrative reviews. Randomized controlled trials (RCTs) are more reliable than observational studies, which, in turn, are more reliable than cohort and case-control studies. The hierarchy of evidence is primarily based on susceptibility to bias (eg, spectrum bias, operational bias, selection bias). Systematic reviews of RCTs are at the top of the hierarchy because they are least susceptible to bias. However, the design and execution of each study can influence susceptibility to bias, so having a method to critically appraise a study is important.

Scoring tools are now available (eg, QUADAS-2 [Quality Assessment of Diagnostic Accuracy Studies] for diagnostic accuracy studies) to guide the critical appraisal of many types of studies; however, these tools require that studies fully report the methods by which the results were obtained. This need has led to the development of reporting guidelines (eg, STARD [Standards for Reporting of Diagnostic Accuracy Studies] for diagnostic accuracy studies and REMARK [Reporting Recommendations for Tumor Marker Prognostic Studies] for prognostic biomarker studies) that list items that should be reported, to allow for critical appraisal. A full list of reporting guidelines is available at the EQUATOR website. 7

Many journals now require authors to demonstrate that they have followed an appropriate reporting guideline. Still, some disciplines have been slow to adopt reporting guidelines, and adherence is often poor even when journals require them. 8-10

Quality refers to the certainty of evidence. High-quality evidence is unlikely to be changed, whereas lower-quality evidence may change with future research findings or may not be applicable in different settings. Quality is initially based on the study type and modified with additional considerations. Quality is ranked lower if there are threats to bias, inconsistency between studies, indirectness, imprecision, or publication bias.

Indirectness refers to the applicability of the available evidence to the clinical question and is assessed by determining the relevance of the PICO parameters to the clinical problem at hand. 11 Quality is ranked higher if there is a large effect size.

The GRADE system rates the quality of a body of evidence for a specific outcome using 4 categories (very low, low, moderate, and high). The grading process is transparent and, to the extent possible, standardized and objective. In general, different decision makers should reach similar conclusions regarding the quality of evidence for an outcome. See several publications for a more detailed explanation of the GRADE process for evaluation of evidence. 5 , 11-14 In particular, we draw attention to 2 articles that focus on strength of evidence for diagnostic recommendations. 15 , 16

The strength of recommendation depends on the balance between desirable and undesirable outcomes. This assessment depends on relative effect sizes, preferences of patients, and the confidence in the estimates of effect sizes and preferences. GRADE provides a systematic and transparent method for assessing the strength of recommendations.

Most publications in laboratory medicine focus on relatively simple outcomes related to analytical effectiveness and clinical performance. 17 However, laboratory guidelines would benefit from familiarity with the GRADE process, to focus evidence on important outcomes. Because they are focused on outcomes, evidence-based recommendations provide a foundation on which laboratories can deliver value.

Abbreviations:

Grading of Recommendations Assessment, Development and Evaluation

population, intervention, comparison, outcome

randomized controlled trial

Quality Assessment of Diagnostic Accuracy Studies

Standards for Reporting of Diagnostic Accuracy Studies

Reporting Recommendations for Tumor Marker Prognostic Studies

Dr Schmidt is an Associate Professor of Pathology at the University of Utah School of Medicine in Salt Lake City. He received his MD, MS in Clinical Epidemiology and his graduate diploma in Biostatistics from the University of Sydney in Australia, an MBA from the University of Chicago in Illinois, and a PhD in Operations Management from the University of Virginia in Charlottesville. He also earned an MS in biochemical engineering from the Massachusetts Institute of Technology in Cambridge and a graduate diploma in Pharmaceutical Medicine from the University of New South Wales in Sydney, Australia. He is board certified in Clinical Pathology and Clinical Informatics. Dr Schmidt is Director of the Center for Effective Medical Testing, which performs studies on cost-effectiveness and evidence-based evaluation of diagnostic testing. He is also Medical Director of Quality Optimization at ARUP Laboratories in Salt Lake City, Utah. His research and clinical activities focus on statistical and economic analysis of diagnostic testing and laboratory operations.

Dr Factor is an Associate Professor of Pathology, the Director of Breast Pathology, and the Co-Director of the Cytopathology Fellowship Program at the University of Utah School of Medicine in Salt Lake City. She received her MHSc degree from Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, and her MD from the Albert Einstein College of Medicine in Bronx, New York, followed by residency and fellowships at Brigham and Women’s Hospital in Boston, Massachusetts. Dr Factor is board certified in Anatomic Pathology and Cytopathology, and is a member of the College of American Pathology, the United States and Canadian Academy of Pathology, and the American Society for Clinical Pathology. Her research interests include the biology and prevention of breast cancer.

Price CP , John AS , Christenson R , et al.  Leveraging the real value of laboratory medicine with the value proposition . Clin Chim Acta . 2016 ; 462 : 183 – 186 .

Google Scholar

Signorelli H , Straseski JA , Genzen JR , Walker BS , Jackson BR , Schmidt RL . Benchmarking to identify practice variation in test ordering: a potential tool for utilization management . Lab Med . 2015 ; 46 : 356 – 364 .

Lin DC , Straseski JA , Schmidt RL , Various T . Multi-Center benchmark study reveals significant variation in thyroid testing in United States . Thyroid 2017 ; 27 ( 10 ): 1232 – 1245 .

Schmidt RL , LoPresti JS , McDermott MT , Zick SM , Straseski JA . Does reverse triiodothyronine testing have clinical utility? an analysis of practice variation based on order data from a national reference laboratory . Thyroid 2018 ; 28 ( 7 ): 242 – 248 .

Guyatt G , Oxman AD , Akl EA , et al.  GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables . J Clin Epidemiol . 2011 ; 64 ( 4 ): 383 – 394 .

Guyatt GH , Oxman AD , Kunz R , et al.  GRADE guidelines: 2. Framing the question and deciding on important outcomes . J Clin Epidemiol . 2011 ; 64 ( 4 ): 395 – 400 .

EQUATOR network . http://www.equator-network.org/ . Accessed October 26, 2018 .

Google Preview

Caron JE , March JK , Cohen MB , Schmidt RL . A survey of the prevalence and impact of reporting guideline endorsement in pathology journals . Am J Clin Pathol . 2017 ; 148 ( 4 ): 314 – 322 .

Hopewell S , Boutron I , Altman DG , Ravaud P . Deficiencies in the publication and reporting of the results of systematic reviews presented at scientific medical conferences . J Clin Epidemiol . 2015 ; 68 ( 12 ): 1488 – 1495 .

Turner L , Shamseer L , Altman DG , Schulz KF , Moher D . Does use of the CONSORT statement impact the completeness of reporting of randomised controlled trials published in medical journals? A Cochrane review . Syst Rev . 2012 ; 1 : 60 .

Guyatt GH , Oxman AD , Kunz R , et al.  ; GRADE Working Group . GRADE guidelines: 8. Rating the quality of evidence–indirectness . J Clin Epidemiol . 2011 ; 64 ( 12 ): 1303 – 1310 .

Guyatt GH , Oxman AD , Sultan S , et al.  ; GRADE Working Group . GRADE guidelines: 9. Rating up the quality of evidence . J Clin Epidemiol . 2011 ; 64 ( 12 ): 1311 – 1316 .

Guyatt GH , Oxman AD , Kunz R , et al.  ; GRADE Working Group . GRADE guidelines: 7. Rating the quality of evidence–inconsistency . J Clin Epidemiol . 2011 ; 64 ( 12 ): 1294 – 1302 .

Schünemann HJ , Oxman AD , Brozek J , et al.  GRADE: assessing the quality of evidence for diagnostic recommendations . ACP J Club . 2008 ; 149 ( 6 ): 2 .

Schünemann HJ , Oxman AD , Brozek J , et al.  GRADE: assessing the quality of evidence for diagnostic recommendations . Evid Based Med . 2008 ; 13 ( 6 ): 162 – 163 .

Trenti T , Schünemann HJ , Plebani M . Developing GRADE outcome-based recommendations about diagnostic tests: a key role in laboratory medicine policies . Clin Chem Lab Med . 2016 ; 54 ( 4 ): 535 – 543 .

Horvath AR , Lord SJ , StJohn A , et al.  ; Test Evaluation Working Group of the European Federation of Clinical Chemistry Laboratory Medicine . From biomarkers to medical tests: the changing landscape of test evaluation . Clin Chim Acta . 2014 ; 427 : 49 – 57 .

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How to write recommendations in a research paper

Many students put in a lot of effort and write a good report however they are not able to give proper recommendations. Recommendations in the research paper should be included in your research. As a researcher, you display a deep understanding of the topic of research. Therefore you should be able to give recommendations. Here are a few tips that will help you to give appropriate recommendations. 

Recommendations in the research paper should be the objective of the research. Therefore at least one of your objectives of the paper is to provide recommendations to the parties associated or the parties that will benefit from your research. For example, to encourage higher employee engagement HR department should make strategies that invest in the well-being of employees. Additionally, the HR department should also collect regular feedback through online surveys.

Recommendations in the research paper should come from your review and analysis For example It was observed that coaches interviewed were associated with the club were working with the club from the past 2-3 years only. This shows that the attrition rate of coaches is high and therefore clubs should work on reducing the turnover of coaches.

Recommendations in the research paper should also come from the data you have analysed. For example, the research found that people over 65 years of age are at greater risk of social isolation. Therefore, it is recommended that policies that are made for combating social isolation should target this specific group.

Recommendations in the research paper should also come from observation. For example, it is observed that Lenovo’s income is stable and gross revenue has displayed a negative turn. Therefore the company should analyse its marketing and branding strategy.

Recommendations in the research paper should be written in the order of priority. The most important recommendations for decision-makers should come first. However, if the recommendations are of equal importance then it should come in the sequence in which the topic is approached in the research. 

Recommendations in a research paper if associated with different categories then you should categorize them. For example, you have separate recommendations for policymakers, educators, and administrators then you can categorize the recommendations. 

Recommendations in the research paper should come purely from your research. For example, you have written research on the impact on HR strategies on motivation. However, nowhere you have discussed Reward and recognition. Then you should not give recommendations for using rewards and recognition measures to boost employee motivation.

The use of bullet points offers better clarity rather than using long paragraphs. For example this paragraph “ It is recommended  that Britannia Biscuit should launch and promote sugar-free options apart from the existing product range. Promotion efforts should be directed at creating a fresh and healthy image. A campaign that conveys a sense of health and vitality to the consumer while enjoying biscuit  is recommended” can be written as:

• The company should launch and promote sugar-free options
• The company should work towards creating s fresh and healthy image
• The company should run a campaign to convey its healthy image

The inclusion of an action plan along with recommendation adds more weightage to your recommendation. Recommendations should be clear and conscience and written using actionable words. Recommendations should display a solution-oriented approach and in some cases should highlight the scope for further research. 

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A systematic review and research perspective on recommender systems

• Deepjyoti Roy   ORCID: orcid.org/0000-0002-8020-7145 1 &
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Journal of Big Data volume  9 , Article number:  59 ( 2022 ) Cite this article

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Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system.

Introduction

The recent advancements in technology along with the prevalence of online services has offered more abilities for accessing a huge amount of online information in a faster manner. Users can post reviews, comments, and ratings for various types of services and products available online. However, the recent advancements in pervasive computing have resulted in an online data overload problem. This data overload complicates the process of finding relevant and useful content over the internet. The recent establishment of several procedures having lower computational requirements can however guide users to the relevant content in a much easy and fast manner. Because of this, the development of recommender systems has recently gained significant attention. In general, recommender systems act as information filtering tools, offering users suitable and personalized content or information. Recommender systems primarily aim to reduce the user’s effort and time required for searching relevant information over the internet.

Nowadays, recommender systems are being increasingly used for a large number of applications such as web [ 1 , 67 , 70 ], books [ 2 ], e-learning [ 4 , 16 , 61 ], tourism [ 5 , 8 , 78 ], movies [ 66 ], music [ 79 ], e-commerce, news, specialized research resources [ 65 ], television programs [ 72 , 81 ], etc. It is therefore important to build high-quality and exclusive recommender systems for providing personalized recommendations to the users in various applications. Despite the various advances in recommender systems, the present generation of recommender systems requires further improvements to provide more efficient recommendations applicable to a broader range of applications. More investigation of the existing latest works on recommender systems is required which focus on diverse applications.

There is hardly any review paper that has categorically synthesized and reviewed the literature of all the classification fields and application domains of recommender systems. The few existing literature reviews in the field cover just a fraction of the articles or focus only on selected aspects such as system evaluation. Thus, they do not provide an overview of the application field, algorithmic categorization, or identify the most promising approaches. Also, review papers often neglect to analyze the dataset description and the simulation platforms used. This paper aims to fulfil this significant gap by reviewing and comparing existing articles on recommender systems based on a defined classification framework, their algorithmic categorization, simulation platforms used, applications focused, their features and challenges, dataset description and system performance. Finally, we provide researchers and practitioners with insight into the most promising directions for further investigation in the field of recommender systems under various applications.

In essence, recommender systems deal with two entities—users and items, where each user gives a rating (or preference value) to an item (or product). User ratings are generally collected by using implicit or explicit methods. Implicit ratings are collected indirectly from the user through the user’s interaction with the items. Explicit ratings, on the other hand, are given directly by the user by picking a value on some finite scale of points or labelled interval values. For example, a website may obtain implicit ratings for different items based on clickstream data or from the amount of time a user spends on a webpage and so on. Most recommender systems gather user ratings through both explicit and implicit methods. These feedbacks or ratings provided by the user are arranged in a user-item matrix called the utility matrix as presented in Table 1 .

The utility matrix often contains many missing values. The problem of recommender systems is mainly focused on finding the values which are missing in the utility matrix. This task is often difficult as the initial matrix is usually very sparse because users generally tend to rate only a small number of items. It may also be noted that we are interested in only the high user ratings because only such items would be suggested back to the users. The efficiency of a recommender system greatly depends on the type of algorithm used and the nature of the data source—which may be contextual, textual, visual etc.

Types of recommender systems

Recommender systems are broadly categorized into three different types viz. content-based recommender systems, collaborative recommender systems and hybrid recommender systems. A diagrammatic representation of the different types of recommender systems is given in Fig.  1 .

Content-based recommender system

In content-based recommender systems, all the data items are collected into different item profiles based on their description or features. For example, in the case of a book, the features will be author, publisher, etc. In the case of a movie, the features will be the movie director, actor, etc. When a user gives a positive rating to an item, then the other items present in that item profile are aggregated together to build a user profile. This user profile combines all the item profiles, whose items are rated positively by the user. Items present in this user profile are then recommended to the user, as shown in Fig.  2 .

One drawback of this approach is that it demands in-depth knowledge of the item features for an accurate recommendation. This knowledge or information may not be always available for all items. Also, this approach has limited capacity to expand on the users' existing choices or interests. However, this approach has many advantages. As user preferences tend to change with time, this approach has the quick capability of dynamically adapting itself to the changing user preferences. Since one user profile is specific only to that user, this algorithm does not require the profile details of any other users because they provide no influence in the recommendation process. This ensures the security and privacy of user data. If new items have sufficient description, content-based techniques can overcome the cold-start problem i.e., this technique can recommend an item even when that item has not been previously rated by any user. Content-based filtering approaches are more common in systems like personalized news recommender systems, publications, web pages recommender systems, etc.

Collaborative filtering-based recommender system

Collaborative approaches make use of the measure of similarity between users. This technique starts with finding a group or collection of user X whose preferences, likes, and dislikes are similar to that of user A. X is called the neighbourhood of A. The new items which are liked by most of the users in X are then recommended to user A. The efficiency of a collaborative algorithm depends on how accurately the algorithm can find the neighbourhood of the target user. Traditionally collaborative filtering-based systems suffer from the cold-start problem and privacy concerns as there is a need to share user data. However, collaborative filtering approaches do not require any knowledge of item features for generating a recommendation. Also, this approach can help to expand on the user’s existing interests by discovering new items. Collaborative approaches are again divided into two types: memory-based approaches and model-based approaches.

Memory-based collaborative approaches recommend new items by taking into consideration the preferences of its neighbourhood. They make use of the utility matrix directly for prediction. In this approach, the first step is to build a model. The model is equal to a function that takes the utility matrix as input.

Model = f (utility matrix)

Then recommendations are made based on a function that takes the model and user profile as input. Here we can make recommendations only to users whose user profile belongs to the utility matrix. Therefore, to make recommendations for a new user, the user profile must be added to the utility matrix, and the similarity matrix should be recomputed, which makes this technique computation heavy.

Recommendation = f (defined model, user profile) where user profile  ∈  utility matrix

Memory-based collaborative approaches are again sub-divided into two types: user-based collaborative filtering and item-based collaborative filtering. In the user-based approach, the user rating of a new item is calculated by finding other users from the user neighbourhood who has previously rated that same item. If a new item receives positive ratings from the user neighbourhood, the new item is recommended to the user. Figure  3 depicts the user-based filtering approach.

User-based collaborative filtering

In the item-based approach, an item-neighbourhood is built consisting of all similar items which the user has rated previously. Then that user’s rating for a different new item is predicted by calculating the weighted average of all ratings present in a similar item-neighbourhood as shown in Fig.  4 .

Item-based collaborative filtering

Model-based systems use various data mining and machine learning algorithms to develop a model for predicting the user’s rating for an unrated item. They do not rely on the complete dataset when recommendations are computed but extract features from the dataset to compute a model. Hence the name, model-based technique. These techniques also need two steps for prediction—the first step is to build the model, and the second step is to predict ratings using a function (f) which takes the model defined in the first step and the user profile as input.

Recommendation = f (defined model, user profile) where user profile  ∉  utility matrix

Model-based techniques do not require adding the user profile of a new user into the utility matrix before making predictions. We can make recommendations even to users that are not present in the model. Model-based systems are more efficient for group recommendations. They can quickly recommend a group of items by using the pre-trained model. The accuracy of this technique largely relies on the efficiency of the underlying learning algorithm used to create the model. Model-based techniques are capable of solving some traditional problems of recommender systems such as sparsity and scalability by employing dimensionality reduction techniques [ 86 ] and model learning techniques.

Hybrid filtering

A hybrid technique is an aggregation of two or more techniques employed together for addressing the limitations of individual recommender techniques. The incorporation of different techniques can be performed in various ways. A hybrid algorithm may incorporate the results achieved from separate techniques, or it can use content-based filtering in a collaborative method or use a collaborative filtering technique in a content-based method. This hybrid incorporation of different techniques generally results in increased performance and increased accuracy in many recommender applications. Some of the hybridization approaches are meta-level, feature-augmentation, feature-combination, mixed hybridization, cascade hybridization, switching hybridization and weighted hybridization [ 86 ]. Table 2 describes these approaches.

Recommender system challenges

This section briefly describes the various challenges present in current recommender systems and offers different solutions to overcome these challenges.

Cold start problem

The cold start problem appears when the recommender system cannot draw any inference from the existing data, which is insufficient. Cold start refers to a condition when the system cannot produce efficient recommendations for the cold (or new) users who have not rated any item or have rated a very few items. It generally arises when a new user enters the system or new items (or products) are inserted into the database. Some solutions to this problem are as follows: (a) Ask new users to explicitly mention their item preference. (b) Ask a new user to rate some items at the beginning. (c) Collect demographic information (or meta-data) from the user and recommend items accordingly.

Shilling attack problem

This problem arises when a malicious user fakes his identity and enters the system to give false item ratings [ 87 ]. Such a situation occurs when the malicious user wants to either increase or decrease some item’s popularity by causing a bias on selected target items. Shilling attacks greatly reduce the reliability of the system. One solution to this problem is to detect the attackers quickly and remove the fake ratings and fake user profiles from the system.

Synonymy problem

This problem arises when similar or related items have different entries or names, or when the same item is represented by two or more names in the system [ 78 ]. For example, babywear and baby cloth. Many recommender systems fail to distinguish these differences, hence reducing their recommendation accuracy. To alleviate this problem many methods are used such as demographic filtering, automatic term expansion and Singular Value Decomposition [ 76 ].

Latency problem

The latency problem is specific to collaborative filtering approaches and occurs when new items are frequently inserted into the database. This problem is characterized by the system’s failure to recommend new items. This happens because new items must be reviewed before they can be recommended in a collaborative filtering environment. Using content-based filtering may resolve this issue, but it may introduce overspecialization and decrease the computing time and system performance. To increase performance, the calculations can be done in an offline environment and clustering-based techniques can be used [ 76 ].

Sparsity problem

Data sparsity is a common problem in large scale data analysis, which arises when certain expected values are missing in the dataset. In the case of recommender systems, this situation occurs when the active users rate very few items. This reduces the recommendation accuracy. To alleviate this problem several techniques can be used such as demographic filtering, singular value decomposition and using model-based collaborative techniques.

Grey sheep problem

The grey sheep problem is specific to pure collaborative filtering approaches where the feedback given by one user do not match any user neighbourhood. In this situation, the system fails to accurately predict relevant items for that user. This problem can be resolved by using pure content-based approaches where predictions are made based on the user’s profile and item properties.

Scalability problem

Recommender systems, especially those employing collaborative filtering techniques, require large amounts of training data, which cause scalability problems. The scalability problem arises when the amount of data used as input to a recommender system increases quickly. In this era of big data, more and more items and users are rapidly getting added to the system and this problem is becoming common in recommender systems. Two common approaches used to solve the scalability problem is dimensionality reduction and using clustering-based techniques to find users in tiny clusters instead of the complete database.

Methodology

The purpose of this study is to understand the research trends in the field of recommender systems. The nature of research in recommender systems is such that it is difficult to confine each paper to a specific discipline. This can be further understood by the fact that research papers on recommender systems are scattered across various journals such as computer science, management, marketing, information technology and information science. Hence, this literature review is conducted over a wide range of electronic journals and research databases such as ACM Portal, IEEE/IEE Library, Google Scholars and Science Direct [ 88 ].

The search process of online research articles was performed based on 6 descriptors: “Recommender systems”, “Recommendation systems”, “Movie Recommend*”, “Music Recommend*”, “Personalized Recommend*”, “Hybrid Recommend*”. The following research papers described below were excluded from our research:

News articles.

Master’s dissertations.

Non-English papers.

Unpublished papers.

Research papers published before 2011.

We have screened a total of 350 articles based on their abstracts and content. However, only research papers that described how recommender systems can be applied were chosen. Finally, 60 papers were selected from top international journals indexed in Scopus or E-SCI in 2021. We now present the PRISMA flowchart of the inclusion and exclusion process in Fig.  5 .

PRISMA flowchart of the inclusion and exclusion process. Abstract and content not suitable to the study: * The use or application of the recommender system is not specified: **

Each paper was carefully reviewed and classified into 6 categories in the application fields and 3 categories in the techniques used to develop the system. The classification framework is presented in Fig.  6 .

Classification framework

The number of relevant articles come from Expert Systems with Applications (23%), followed by IEEE (17%), Knowledge-Based System (17%) and Others (43%). Table 3 depicts the article distribution by journal title and Table 4 depicts the sector-wise article distribution.

Both forward and backward searching techniques were implemented to establish that the review of 60 chosen articles can represent the domain literature. Hence, this paper can demonstrate its validity and reliability as a literature review.

Review on state-of-the-art recommender systems

This section presents a state-of-art literature review followed by a chronological review of the various existing recommender systems.

Literature review

In 2011, Castellano et al. [ 1 ] developed a “NEuro-fuzzy WEb Recommendation (NEWER)” system for exploiting the possibility of combining computational intelligence and user preference for suggesting interesting web pages to the user in a dynamic environment. It considered a set of fuzzy rules to express the correlations between user relevance and categories of pages. Crespo et al. [ 2 ] presented a recommender system for distance education over internet. It aims to recommend e-books to students using data from user interaction. The system was developed using a collaborative approach and focused on solving the data overload problem in big digital content. Lin et al. [ 3 ] have put forward a recommender system for automatic vending machines using Genetic algorithm (GA), k-means, Decision Tree (DT) and Bayesian Network (BN). It aimed at recommending localized products by developing a hybrid model combining statistical methods, classification methods, clustering methods, and meta-heuristic methods. Wang and Wu [ 4 ] have implemented a ubiquitous learning system for providing personalized learning assistance to the learners by combining the recommendation algorithm with a context-aware technique. It employed the Association Rule Mining (ARM) technique and aimed to increase the effectiveness of the learner’s learning. García-Crespo et al. [ 5 ] presented a “semantic hotel” recommender system by considering the experiences of consumers using a fuzzy logic approach. The system considered both hotel and customer characteristics. Dong et al. [ 6 ] proposed a structure for a service-concept recommender system using a semantic similarity model by integrating the techniques from the view of an ontology structure-oriented metric and a concept content-oriented metric. The system was able to deliver optimal performance when compared with similar recommender systems. Li et al. [ 7 ] developed a Fuzzy linguistic modelling-based recommender system for assisting users to find experts in knowledge management systems. The developed system was applied to the aircraft industry where it demonstrated efficient and feasible performance. Lorenzi et al. [ 8 ] presented an “assumption-based multiagent” system to make travel package recommendations using user preferences in the tourism industry. It performed different tasks like discovering, filtering, and integrating specific information for building a travel package following the user requirement. Huang et al. [ 9 ] proposed a context-aware recommender system through the extraction, evaluation and incorporation of contextual information gathered using the collaborative filtering and rough set model.

In 2012, Chen et al. [ 10 ] presented a diabetes medication recommender model by using “Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS)” for aggregating suitable prescriptions for the patients. It aimed at selecting the most suitable drugs from the list of specific drugs. Mohanraj et al. [ 11 ] developed the “Ontology-driven bee’s foraging approach (ODBFA)” to accurately predict the online navigations most likely to be visited by a user. The self-adaptive system is intended to capture the various requirements of the online user by using a scoring technique and by performing a similarity comparison. Hsu et al. [ 12 ] proposed a “personalized auxiliary material” recommender system by considering the specific course topics, individual learning styles, complexity of the auxiliary materials using an artificial bee colony algorithm. Gemmell et al. [ 13 ] demonstrated a solution for the problem of resource recommendation in social annotation systems. The model was developed using a linear-weighted hybrid method which was capable of providing recommendations under different constraints. Choi et al. [ 14 ] proposed one “Hybrid Online-Product rEcommendation (HOPE) system” by the integration of collaborative filtering through sequential pattern analysis-based recommendations and implicit ratings. Garibaldi et al. [ 15 ] put forward a technique for incorporating the variability in a fuzzy inference model by using non-stationary fuzzy sets for replicating the variabilities of a human. This model was applied to a decision problem for treatment recommendations of post-operative breast cancer.

In 2013, Salehi and Kmalabadi [ 16 ] proposed an e-learning material recommender system by “modelling of materials in a multidimensional space of material’s attribute”. It employed both content and collaborative filtering. Aher and Lobo [ 17 ] introduced a course recommender system using data mining techniques such as simple K-means clustering and Association Rule Mining (ARM) algorithm. The proposed e-learning system was successfully demonstrated for “MOOC (Massively Open Online Courses)”. Kardan and Ebrahimi [ 18 ] developed a hybrid recommender system for recommending posts in asynchronous discussion groups. The system was built combining both collaborative filtering and content-based filtering. It considered implicit user data to compute the user similarity with various groups, for recommending suitable posts and contents to its users. Chang et al. [ 19 ] adopted a cloud computing technology for building a TV program recommender system. The system designed for digital TV programs was implemented using Hadoop Fair Scheduler (HFC), K-means clustering and k-nearest neighbour (KNN) algorithms. It was successful in processing huge amounts of real-time user data. Lucas et al. [ 20 ] implemented a recommender model for assisting a tourism application by using associative classification and fuzzy logic to predict the context. Niu et al. [ 21 ] introduced “Affivir: An Affect-based Internet Video Recommendation System” which was developed by calculating user preferences and by using spectral clustering. This model recommended videos with similar effects, which was processed to get optimal results with dynamic adjustments of recommendation constraints.

In 2014, Liu et al. [ 22 ] implemented a new route recommendation model for offering personalized and real-time route recommendations for self-driven tourists to minimize the queuing time and traffic jams infamous tourist places. Recommendations were carried out by considering the preferences of users. Bakshi et al. [ 23 ] proposed an unsupervised learning-based recommender model for solving the scalability problem of recommender systems. The algorithm used transitive similarities along with Particle Swarm Optimization (PSO) technique for discovering the global neighbours. Kim and Shim [ 24 ] proposed a recommender system based on “latent Dirichlet allocation using probabilistic modelling for Twitter” that could recommend the top-K tweets for a user to read, and the top-K users to follow. The model parameters were learned from an inference technique by using the differential Expectation–Maximization (EM) algorithm. Wang et al. [ 25 ] developed a hybrid-movie recommender model by aggregating a genetic algorithm (GA) with improved K-means and Principal Component Analysis (PCA) technique. It was able to offer intelligent movie recommendations with personalized suggestions. Kolomvatsos et al. [ 26 ] proposed a recommender system by considering an optimal stopping theory for delivering books or music recommendations to the users. Gottschlich et al. [ 27 ] proposed a decision support system for stock investment recommendations. It computed the output by considering the overall crowd’s recommendations. Torshizi et al. [ 28 ] have introduced a hybrid recommender system to determine the severity level of a medical condition. It could recommend suitable therapies for patients suffering from Benign Prostatic Hyperplasia.

In 2015, Zahálka et al. [ 29 ] proposed a venue recommender: “City Melange”. It was an interactive content-based model which used the convolutional deep-net features of the visual domain and the linear Support Vector Machine (SVM) model to capture the semantic information and extract latent topics. Sankar et al. [ 30 ] have proposed a stock recommender system based on the stock holding portfolio of trusted mutual funds. The system employed the collaborative filtering approach along with social network analysis for offering a decision support system to build a trust-based recommendation model. Chen et al. [ 31 ] have put forward a novel movie recommender system by applying the “artificial immune network to collaborative filtering” technique. It computed the affinity of an antigen and the affinity between an antibody and antigen. Based on this computation a similarity estimation formula was introduced which was used for the movie recommendation process. Wu et al. [ 32 ] have examined the technique of data fusion for increasing the efficiency of item recommender systems. It employed a hybrid linear combination model and used a collaborative tagging system. Yeh and Cheng [ 33 ] have proposed a recommender system for tourist attractions by constructing the “elicitation mechanism using the Delphi panel method and matrix construction mechanism using the repertory grids”, which was developed by considering the user preference and expert knowledge.

In 2016, Liao et al. [ 34 ] proposed a recommender model for online customers using a rough set association rule. The model computed the probable behavioural variations of online consumers and provided product category recommendations for e-commerce platforms. Li et al. [ 35 ] have suggested a movie recommender system based on user feedback collected from microblogs and social networks. It employed the sentiment-aware association rule mining algorithm for recommendations using the prior information of frequent program patterns, program metadata similarity and program view logs. Wu et al. [ 36 ] have developed a recommender system for social media platforms by aggregating the technique of Social Matrix Factorization (SMF) and Collaborative Topic Regression (CTR). The model was able to compute the ratings of users to items for making recommendations. For improving the recommendation quality, it gathered information from multiple sources such as item properties, social networks, feedback, etc. Adeniyi et al. [ 37 ] put forward a study of automated web-usage data mining and developed a recommender system that was tested in both real-time and online for identifying the visitor’s or client’s clickstream data.

In 2017, Rawat and Kankanhalli [ 38 ] have proposed a viewpoint recommender system called “ClickSmart” for assisting mobile users to capture high-quality photographs at famous tourist places. Yang et al. [ 39 ] proposed a gradient boosting-based job recommendation system for satisfying the cost-sensitive requirements of the users. The hybrid algorithm aimed to reduce the rate of unnecessary job recommendations. Lee et al. [ 40 ] proposed a music streaming recommender system based on smartphone activity usage. The proposed system benefitted by using feature selection approaches with machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM), Multi-layer Perception (MLP), Instance-based k -Nearest Neighbour (IBK), and Random Forest (RF) for performing the activity detection from the mobile signals. Wei et al. [ 41 ] have proposed a new stacked denoising autoencoder (SDAE) based recommender system for cold items. The algorithm employed deep learning and collaborative filtering method to predict the unknown ratings.

In 2018, Li et al. [ 42 ] have developed a recommendation algorithm using Weighted Linear Regression Models (WLRRS). The proposed system was put to experiment using the MovieLens dataset and it presented better classification and predictive accuracy. Mezei and Nikou [ 43 ] presented a mobile health and wellness recommender system based on fuzzy optimization. It could recommend a collection of actions to be taken by the user to improve the user’s health condition. Recommendations were made considering the user’s physical activities and preferences. Ayata et al. [ 44 ] proposed a music recommendation model based on the user emotions captured through wearable physiological sensors. The emotion detection algorithm employed different machine learning algorithms like SVM, RF, KNN and decision tree (DT) algorithms to predict the emotions from the changing electrical signals gathered from the wearable sensors. Zhao et al. [ 45 ] developed a multimodal learning-based, social-aware movie recommender system. The model was able to successfully resolve the sparsity problem of recommender systems. The algorithm developed a heterogeneous network by exploiting the movie-poster image and textual description of each movie based on the social relationships and user ratings.

In 2019, Hammou et al. [ 46 ] proposed a Big Data recommendation algorithm capable of handling large scale data. The system employed random forest and matrix factorization through a data partitioning scheme. It was then used for generating recommendations based on user rating and preference for each item. The proposed system outperformed existing systems in terms of accuracy and speed. Zhao et al. [ 47 ] have put forward a hybrid initialization method for social network recommender systems. The algorithm employed denoising autoencoder (DAE) neural network-based initialization method (ANNInit) and attribute mapping. Bhaskaran and Santhi [ 48 ] have developed a hybrid, trust-based e-learning recommender system using cloud computing. The proposed algorithm was capable of learning online user activities by using the Firefly Algorithm (FA) and K-means clustering. Afolabi and Toivanen [ 59 ] have suggested an integrated recommender model based on collaborative filtering. The proposed model “Connected Health for Effective Management of Chronic Diseases”, aimed for integrating recommender systems for better decision-making in the process of disease management. He et al. [ 60 ] proposed a movie recommender system called “HI2Rec” which explored the usage of collaborative filtering and heterogeneous information for making movie recommendations. The model used the knowledge representation learning approach to embed movie-related information gathered from different sources.

In 2020, Han et al. [ 49 ] have proposed one Internet of Things (IoT)-based cancer rehabilitation recommendation system using the Beetle Antennae Search (BAS) algorithm. It presented the patients with a solution for the problem of optimal nutrition program by considering the objective function as the recurrence time. Kang et al. [ 50 ] have presented a recommender system for personalized advertisements in Online Broadcasting based on a tree model. Recommendations were generated in real-time by considering the user preferences to minimize the overhead of preference prediction and using a HashMap along with the tree characteristics. Ullah et al. [ 51 ] have implemented an image-based service recommendation model for online shopping based random forest and Convolutional Neural Networks (CNN). The model used JPEG coefficients to achieve an accurate prediction rate. Cai et al. [ 52 ] proposed a new hybrid recommender model using a many-objective evolutionary algorithm (MaOEA). The proposed algorithm was successful in optimizing the novelty, diversity, and accuracy of recommendations. Esteban et al. [ 53 ] have implemented a hybrid multi-criteria recommendation system concerned with students’ academic performance, personal interests, and course selection. The system was developed using a Genetic Algorithm (GA) and aimed at helping university students. It combined both course information and student information for increasing system performance and the reliability of the recommendations. Mondal et al. [ 54 ] have built a multilayer, graph data model-based doctor recommendation system by exploiting the trust concept between a patient-doctor relationship. The proposed system showed good results in practical applications.

In 2021, Dhelim et al. [ 55 ] have developed a personality-based product recommending model using the techniques of meta path discovery and user interest mining. This model showed better results when compared to session-based and deep learning models. Bhalse et al. [ 56 ] proposed a web-based movie recommendation system based on collaborative filtering using Singular Value Decomposition (SVD), collaborative filtering and cosine similarity (CS) for addressing the sparsity problem of recommender systems. It suggested a recommendation list by considering the content information of movies. Similarly, to solve both sparsity and cold-start problems Ke et al. [ 57 ] proposed a dynamic goods recommendation system based on reinforcement learning. The proposed system was capable of learning from the reduced entropy loss error on real-time applications. Chen et al. [ 58 ] have presented a movie recommender model combining various techniques like user interest with category-level representation, neighbour-assisted representation, user interest with latent representation and item-level representation using Feed-forward Neural Network (FNN).

Comparative chronological review

A comparative chronological review to compare the total contributions on various recommender systems in the past 10 years is given in Fig.  7 .

Comparative chronological review of recommender systems under diverse applications

This review puts forward a comparison of the number of research works proposed in the domain of recommender systems from the year 2011 to 2021 using various deep learning and machine learning-based approaches. Research articles are categorized based on the recommender system classification framework as shown in Table 5 . The articles are ordered according to their year of publication. There are two key concepts: Application fields and techniques used. The application fields of recommender systems are divided into six different fields, viz. entertainment, health, tourism, web/e-commerce, education and social media/others.

Algorithmic categorization, simulation platforms and applications considered for various recommender systems

This section analyses different methods like deep learning, machine learning, clustering and meta-heuristic-based-approaches used in the development of recommender systems. The algorithmic categorization of different recommender systems is given in Fig.  8 .

Algorithmic categorization of different recommender systems

Categorization is done based on content-based, collaborative filtering-based, and optimization-based approaches. In [ 8 ], a content-based filtering technique was employed for increasing the ability to trust other agents and for improving the exchange of information by trust degree. In [ 16 ], it was applied to enhance the quality of recommendations using the account attributes of the material. It achieved better performance concerning with F1-score, recall and precision. In [ 18 ], this technique was able to capture the implicit user feedback, increasing the overall accuracy of the proposed model. The content-based filtering in [ 30 ] was able to increase the accuracy and performance of a stock recommender system by using the “trust factor” for making decisions.

Different collaborative filtering approaches are utilized in recent studies, which are categorized as follows:

Model-based techniques

Neuro-Fuzzy [ 1 ] based technique helps in discovering the association between user categories and item relevance. It is also simple to understand. K-Means Clustering [ 2 , 19 , 25 , 48 ] is efficient for large scale datasets. It is simple to implement and gives a fast convergence rate. It also offers automatic recovery from failures. The decision tree [ 2 , 44 ] technique is easy to interpret. It can be used for solving the classic regression and classification problems in recommender systems. Bayesian Network [ 3 ] is a probabilistic technique used to solve classification challenges. It is based on the theory of Bayes theorem and conditional probability. Association Rule Mining (ARM) techniques [ 4 , 17 , 35 ] extract rules for projecting the occurrence of an item by considering the existence of other items in a transaction. This method uses the association rules to create a more suitable representation of data and helps in increasing the model performance and storage efficiency. Fuzzy Logic [ 5 , 7 , 15 , 20 , 28 , 43 ] techniques use a set of flexible rules. It focuses on solving complex real-time problems having an inaccurate spectrum of data. This technique provides scalability and helps in increasing the overall model performance for recommender systems. The semantic similarity [ 6 ] technique is used for describing a topological similarity to define the distance among the concepts and terms through ontologies. It measures the similarity information for increasing the efficiency of recommender systems. Rough set [ 9 , 34 ] techniques use probability distributions for solving the challenges of existing recommender models. Semantic web rule language [ 10 ] can efficiently extract the dataset features and increase the model efficiency. Linear programming-based approaches [ 13 , 42 ] are employed for achieving quality decision making in recommender models. Sequential pattern analysis [ 14 ] is applied to find suitable patterns among data items. This helps in increasing model efficiency. The probabilistic model [ 24 ] is a famous tool to handle uncertainty in risk computations and performance assessment. It offers better decision-making capabilities. K-nearest neighbours (KNN) [ 19 , 37 , 44 ] technique provides faster computation time, simplicity and ease of interpretation. They are good for classification and regression-based problems and offers more accuracy. Spectral clustering [ 21 ] is also called graph clustering or similarity-based clustering, which mainly focuses on reducing the space dimensionality in identifying the dataset items. Stochastic learning algorithm [ 26 ] solves the real-time challenges of recommender systems. Linear SVM [ 29 , 44 ] efficiently solves the high dimensional problems related to recommender systems. It is a memory-efficient method and works well with a large number of samples having relative separation among the classes. This method has been shown to perform well even when new or unfamiliar data is added. Relational Functional Gradient Boosting [ 39 ] technique efficiently works on the relational dependency of data, which is useful for statical relational learning for collaborative-based recommender systems. Ensemble learning [ 40 ] combines the forecast of two or more models and aims to achieve better performance than any of the single contributing models. It also helps in reducing overfitting problems, which are common in recommender systems.

SDAE [ 41 ] is used for learning the non-linear transformations with different filters for finding suitable data. This aids in increasing the performance of recommender models. Multimodal network learning [ 45 ] is efficient for multi-modal data, representing a combined representation of diverse modalities. Random forest [ 46 , 51 ] is a commonly used approach in comparison with other classifiers. It has been shown to increase accuracy when handling big data. This technique is a collection of decision trees to minimize variance through training on diverse data samples. ANNInit [ 47 ] is a type of artificial neural network-based technique that has the capability of self-learning and generating efficient results. It is independent of the data type and can learn data patterns automatically. HashMap [ 50 ] gives faster access to elements owing to the hashing methodology, which decreases the data processing time and increases the performance of the system. CNN [ 51 ] technique can automatically fetch the significant features of a dataset without any supervision. It is a computationally efficient method and provides accurate recommendations. This technique is also simple and fast for implementation. Multilayer graph data model [ 54 ] is efficient for real-time applications and minimizes the access time through mapping the correlation as edges among nodes and provides superior performance. Singular Value Decomposition [ 56 ] can simplify the input data and increase the efficiency of recommendations by eliminating the noise present in data. Reinforcement learning [ 57 ] is efficient for practical scenarios of recommender systems having large data sizes. It is capable of boosting the model performance by increasing the model accuracy even for large scale datasets. FNN [ 58 ] is one of the artificial neural network techniques which can learn non-linear and complex relationships between items. It has demonstrated a good performance increase when employed in different recommender systems. Knowledge representation learning [ 60 ] systems aim to simplify the model development process by increasing the acquisition efficiency, inferential efficiency, inferential adequacy and representation adequacy. User-based approaches [ 2 , 55 , 59 ] specialize in detecting user-related meta-data which is employed to increase the overall model performance. This technique is more suitable for real-time applications where it can capture user feedback and use it to increase the user experience.

Optimization-based techniques

The Foraging Bees [ 11 ] technique enables both functional and combinational optimization for random searching in recommender models. Artificial bee colony [ 12 ] is a swarm-based meta-heuristic technique that provides features like faster convergence rate, the ability to handle the objective with stochastic nature, ease for incorporating with other algorithms, usage of fewer control parameters, strong robustness, high flexibility and simplicity. Particle Swarm Optimization [ 23 ] is a computation optimization technique that offers better computational efficiency, robustness in control parameters, and is easy and simple to implement in recommender systems. Portfolio optimization algorithm [ 27 ] is a subclass of optimization algorithms that find its application in stock investment recommender systems. It works well in real-time and helps in the diversification of the portfolio for maximum profit. The artificial immune system [ 31 ]a is computationally intelligent machine learning technique. This technique can learn new patterns in the data and optimize the overall system parameters. Expectation maximization (EM) [ 32 , 36 , 38 ] is an iterative algorithm that guarantees the likelihood of finding the maximum parameters when the input variables are unknown. Delphi panel and repertory grid [ 33 ] offers efficient decision making by solving the dimensionality problem and data sparsity issues of recommender systems. The Firefly algorithm (FA) [ 48 ] provides fast results and increases recommendation efficiency. It is capable of reducing the number of iterations required to solve specific recommender problems. It also provides both local and global sets of solutions. Beetle Antennae Search (BAS) [ 49 ] offers superior search accuracy and maintains less time complexity that promotes the performance of recommendations. Many-objective evolutionary algorithm (MaOEA) [ 52 ] is applicable for real-time, multi-objective, search-related recommender systems. The introduction of a local search operator increases the convergence rate and gets suitable results. Genetic Algorithm (GA) [ 2 , 22 , 25 , 53 ] based techniques are used to solve the multi-objective optimization problems of recommender systems. They employ probabilistic transition rules and have a simpler operation that provides better recommender performance.

Features and challenges

The features and challenges of the existing recommender models are given in Table 6 .

Simulation platforms

The various simulation platforms used for developing different recommender systems with different applications are given in Fig.  9 .

Simulation platforms used for developing different recommender systems

Here, the Java platform is used in 20% of the contributions, MATLAB is implemented in 7% of the contributions, different fold cross-validation are used in 8% of the contributions, 7% of the contributions are utilized by the python platform, 3% of the contributions employ R-programming and 1% of the contributions are developed by Tensorflow, Weka and Android environments respectively. Other simulation platforms like Facebook, web UI (User Interface), real-time environments, etc. are used in 50% of the contributions. Table 7 describes some simulation platforms commonly used for developing recommender systems.

Application focused and dataset description

This section provides an analysis of the different applications focused on a set of recent recommender systems and their dataset details.

Recent recommender systems were analysed and found that 11% of the contributions are focused on the domain of healthcare, 10% of the contributions are on movie recommender systems, 5% of the contributions come from music recommender systems, 6% of the contributions are focused on e-learning recommender systems, 8% of the contributions are used for online product recommender systems, 3% of the contributions are focused on book recommendations and 1% of the contributions are focused on Job and knowledge management recommender systems. 5% of the contributions concentrated on social network recommender systems, 10% of the contributions are focused on tourist and hotels recommender systems, 6% of the contributions are employed for stock recommender systems, and 3% of the contributions contributed for video recommender systems. The remaining 12% of contributions are miscellaneous recommender systems like Twitter, venue-based recommender systems, etc. Similarly, different datasets are gathered for recommender systems based on their application types. A detailed description is provided in Table 8 .

Performance analysis of state-of-art recommender systems

The performance evaluation metrics used for the analysis of different recommender systems is depicted in Table 9 . From the set of research works, 35% of the works use recall measure, 16% of the works employ Mean Absolute Error (MAE), 11% of the works take Root Mean Square Error (RMSE), 41% of the papers consider precision, 30% of the contributions analyse F1-measure, 31% of the works apply accuracy and 6% of the works employ coverage measure to validate the performance of the recommender systems. Moreover, some additional measures are also considered for validating the performance in a few applications.

Research gaps and challenges

In the recent decade, recommender systems have performed well in solving the problem of information overload and has become the more appropriate tool for multiple areas such as psychology, mathematics, computer science, etc. [ 80 ]. However, current recommender systems face a variety of challenges which are stated as follows, and discussed below:

Deployment challenges such as cold start, scalability, sparsity, etc. are already discussed in Sect. 3.

Challenges faced when employing different recommender algorithms for different applications.

Challenges in collecting implicit user data

Challenges in handling real-time user feedback.

Challenges faced in choosing the correct implementation techniques.

Challenges faced in measuring system performance.

Challenges in implementing recommender system for diverse applications.

Numerous recommender algorithms have been proposed on novel emerging dimensions which focus on addressing the existing limitations of recommender systems. A good recommender system must increase the recommendation quality based on user preferences. However, a specific recommender algorithm is not always guaranteed to perform equally for different applications. This encourages the possibility of employing different recommender algorithms for different applications, which brings along a lot of challenges. There is a need for more research to alleviate these challenges. Also, there is a large scope of research in recommender applications that incorporate information from different interactive online sites like Facebook, Twitter, shopping sites, etc. Some other areas for emerging research may be in the fields of knowledge-based recommender systems, methods for seamlessly processing implicit user data and handling real-time user feedback to recommend items in a dynamic environment.

Some of the other research areas like deep learning-based recommender systems, demographic filtering, group recommenders, cross-domain techniques for recommender systems, and dimensionality reduction techniques are also further required to be studied [ 83 ]. Deep learning-based recommender systems have recently gained much popularity. Future research areas in this field can integrate the well-performing deep learning models with new variants of hybrid meta-heuristic approaches.

During this review, it was observed that even though recent recommender systems have demonstrated good performance, there is no single standardized criteria or method which could be used to evaluate the performance of all recommender systems. System performance is generally measured by different evaluation matrices which makes it difficult to compare. The application of recommender systems in real-time applications is growing. User satisfaction and personalization play a very important role in the success of such recommender systems. There is a need for some new evaluation criteria which can evaluate the level of user satisfaction in real-time. New research should focus on capturing real-time user feedback and use the information to change the recommendation process accordingly. This will aid in increasing the quality of recommendations.

Conclusion and future scope

Recommender systems have attracted the attention of researchers and academicians. In this paper, we have identified and prudently reviewed research papers on recommender systems focusing on diverse applications, which were published between 2011 and 2021. This review has gathered diverse details like different application fields, techniques used, simulation tools used, diverse applications focused, performance metrics, datasets used, system features, and challenges of different recommender systems. Further, the research gaps and challenges were put forward to explore the future research perspective on recommender systems. Overall, this paper provides a comprehensive understanding of the trend of recommender systems-related research and to provides researchers with insight and future direction on recommender systems. The results of this study have several practical and significant implications:

Based on the recent-past publication rates, we feel that the research of recommender systems will significantly grow in the future.

A large number of research papers were identified in movie recommendations, whereas health, tourism and education-related recommender systems were identified in very few numbers. This is due to the availability of movie datasets in the public domain. Therefore, it is necessary to develop datasets in other fields also.

There is no standard measure to compute the performance of recommender systems. Among 60 papers, 21 used recall, 10 used MAE, 25 used precision, 18 used F1-measure, 19 used accuracy and only 7 used RMSE to calculate system performance. Very few systems were found to excel in two or more matrices.

Java and Python (with a combined contribution of 27%) are the most common programming languages used to develop recommender systems. This is due to the availability of a large number of standard java and python libraries which aid in the development process.

Recently a large number of hybrid and optimizations techniques are being proposed for recommender systems. The performance of a recommender system can be greatly improved by applying optimization techniques.

There is a large scope of research in using neural networks and deep learning-based methods for developing recommender systems. Systems developed using these methods are found to achieve high-performance accuracy.

This research will provide a guideline for future research in the domain of recommender systems. However, this research has some limitations. Firstly, due to the limited amount of manpower and time, we have only reviewed papers published in journals focusing on computer science, management and medicine. Secondly, we have reviewed only English papers. New research may extend this study to cover other journals and non-English papers. Finally, this review was conducted based on a search on only six descriptors: “Recommender systems”, “Recommendation systems”, “Movie Recommend*”, “Music Recommend*”, “Personalized Recommend*” and “Hybrid Recommend*”. Research papers that did not include these keywords were not considered. Future research can include adding some additional descriptors and keywords for searching. This will allow extending the research to cover more diverse articles on recommender systems.

Availability of data and materials

Not applicable.

Castellano G, Fanelli AM, Torsello MA. NEWER: A system for neuro-fuzzy web recommendation. Appl Soft Comput. 2011;11:793–806.

Article   Google Scholar  

Crespo RG, Martínez OS, Lovelle JMC, García-Bustelo BCP, Gayo JEL, Pablos PO. Recommendation system based on user interaction data applied to intelligent electronic books. Computers Hum Behavior. 2011;27:1445–9.

Lin FC, Yu HW, Hsu CH, Weng TC. Recommendation system for localized products in vending machines. Expert Syst Appl. 2011;38:9129–38.

Wang SL, Wu CY. Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Syst Appl. 2011;38:10831–8.

García-Crespo Á, López-Cuadrado JL, Colomo-Palacios R, González-Carrasco I, Ruiz-Mezcua B. Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain. Expert Syst Appl. 2011;38:13310–9.

Dong H, Hussain FK, Chang E. A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment. J Netw Comput Appl. 2011;34:619–31.

Li M, Liu L, Li CB. An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems. Expert Syst Appl. 2011;38:8586–96.

Lorenzi F, Bazzan ALC, Abel M, Ricci F. Improving recommendations through an assumption-based multiagent approach: An application in the tourism domain. Expert Syst Appl. 2011;38:14703–14.

Huang Z, Lu X, Duan H. Context-aware recommendation using rough set model and collaborative filtering. Artif Intell Rev. 2011;35:85–99.

Chen RC, Huang YH, Bau CT, Chen SM. A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Syst Appl. 2012;39:3995–4006.

Mohanraj V, Chandrasekaran M, Senthilkumar J, Arumugam S, Suresh Y. Ontology driven bee’s foraging approach based self-adaptive online recommendation system. J Syst Softw. 2012;85:2439–50.

Hsu CC, Chen HC, Huang KK, Huang YM. A personalized auxiliary material recommendation system based on learning style on facebook applying an artificial bee colony algorithm. Comput Math Appl. 2012;64:1506–13.

Gemmell J, Schimoler T, Mobasher B, Burke R. Resource recommendation in social annotation systems: A linear-weighted hybrid approach. J Comput Syst Sci. 2012;78:1160–74.

Article   MathSciNet   Google Scholar  

Choi K, Yoo D, Kim G, Suh Y. A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis. Electron Commer Res Appl. 2012;11:309–17.

Garibaldi JM, Zhou SM, Wang XY, John RI, Ellis IO. Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models. J Biomed Inform. 2012;45:447–59.

Salehi M, Kmalabadi IN. A hybrid attribute–based recommender system for e–learning material recommendation. IERI Procedia. 2012;2:565–70.

Aher SB, Lobo LMRJ. Combination of machine learning algorithms for recommendation of courses in e-learning System based on historical data. Knowl-Based Syst. 2013;51:1–14.

Kardan AA, Ebrahimi M. A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Inf Sci. 2013;219:93–110.

Chang JH, Lai CF, Wang MS, Wu TY. A cloud-based intelligent TV program recommendation system. Comput Electr Eng. 2013;39:2379–99.

Lucas JP, Luz N, Moreno MN, Anacleto R, Figueiredo AA, Martins C. A hybrid recommendation approach for a tourism system. Expert Syst Appl. 2013;40:3532–50.

Niu J, Zhu L, Zhao X, Li H. Affivir: An affect-based Internet video recommendation system. Neurocomputing. 2013;120:422–33.

Liu L, Xu J, Liao SS, Chen H. A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication. Expert Syst Appl. 2014;41:3409–17.

Bakshi S, Jagadev AK, Dehuri S, Wang GN. Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization. Appl Soft Comput. 2014;15:21–9.

Kim Y, Shim K. TWILITE: A recommendation system for twitter using a probabilistic model based on latent Dirichlet allocation. Inf Syst. 2014;42:59–77.

Wang Z, Yu X, Feng N, Wang Z. An improved collaborative movie recommendation system using computational intelligence. J Vis Lang Comput. 2014;25:667–75.

Kolomvatsos K, Anagnostopoulos C, Hadjiefthymiades S. An efficient recommendation system based on the optimal stopping theory. Expert Syst Appl. 2014;41:6796–806.

Gottschlich J, Hinz O. A decision support system for stock investment recommendations using collective wisdom. Decis Support Syst. 2014;59:52–62.

Torshizi AD, Zarandi MHF, Torshizi GD, Eghbali K. A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for benign prostatic hyperplasia. Comput Methods Programs Biomed. 2014;113:301–13.

Zahálka J, Rudinac S, Worring M. Interactive multimodal learning for venue recommendation. IEEE Trans Multimedia. 2015;17:2235–44.

Sankar CP, Vidyaraj R, Kumar KS. Trust based stock recommendation system – a social network analysis approach. Procedia Computer Sci. 2015;46:299–305.

Chen MH, Teng CH, Chang PC. Applying artificial immune systems to collaborative filtering for movie recommendation. Adv Eng Inform. 2015;29:830–9.

Wu H, Pei Y, Li B, Kang Z, Liu X, Li H. Item recommendation in collaborative tagging systems via heuristic data fusion. Knowl-Based Syst. 2015;75:124–40.

Yeh DY, Cheng CH. Recommendation system for popular tourist attractions in Taiwan using delphi panel and repertory grid techniques. Tour Manage. 2015;46:164–76.

Liao SH, Chang HK. A rough set-based association rule approach for a recommendation system for online consumers. Inf Process Manage. 2016;52:1142–60.

Li H, Cui J, Shen B, Ma J. An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing. 2016;210:164–73.

Wu H, Yue K, Pei Y, Li B, Zhao Y, Dong F. Collaborative topic regression with social trust ensemble for recommendation in social media systems. Knowl-Based Syst. 2016;97:111–22.

Adeniyi DA, Wei Z, Yongquan Y. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl Computing Inform. 2016;12:90–108.

Rawat YS, Kankanhalli MS. ClickSmart: A context-aware viewpoint recommendation system for mobile photography. IEEE Trans Circuits Syst Video Technol. 2017;27:149–58.

Yang S, Korayem M, Aljadda K, Grainger T, Natarajan S. Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowl-Based Syst. 2017;136:37–45.

Lee WP, Chen CT, Huang JY, Liang JY. A smartphone-based activity-aware system for music streaming recommendation. Knowl-Based Syst. 2017;131:70–82.

Wei J, He J, Chen K, Zhou Y, Tang Z. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl. 2017;69:29–39.

Li C, Wang Z, Cao S, He L. WLRRS: A new recommendation system based on weighted linear regression models. Comput Electr Eng. 2018;66:40–7.

Mezei J, Nikou S. Fuzzy optimization to improve mobile health and wellness recommendation systems. Knowl-Based Syst. 2018;142:108–16.

Ayata D, Yaslan Y, Kamasak ME. Emotion based music recommendation system using wearable physiological sensors. IEEE Trans Consum Electron. 2018;64:196–203.

Zhao Z, Yang Q, Lu H, Weninger T. Social-aware movie recommendation via multimodal network learning. IEEE Trans Multimedia. 2018;20:430–40.

Hammou BA, Lahcen AA, Mouline S. An effective distributed predictive model with matrix factorization and random forest for big data recommendation systems. Expert Syst Appl. 2019;137:253–65.

Zhao J, Geng X, Zhou J, Sun Q, Xiao Y, Zhang Z, Fu Z. Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems. Knowl-Based Syst. 2019;166:132–9.

Bhaskaran S, Santhi B. An efficient personalized trust based hybrid recommendation (TBHR) strategy for e-learning system in cloud computing. Clust Comput. 2019;22:1137–49.

Han Y, Han Z, Wu J, Yu Y, Gao S, Hua D, Yang A. Artificial intelligence recommendation system of cancer rehabilitation scheme based on IoT technology. IEEE Access. 2020;8:44924–35.

Kang S, Jeong C, Chung K. Tree-based real-time advertisement recommendation system in online broadcasting. IEEE Access. 2020;8:192693–702.

Ullah F, Zhang B, Khan RU. Image-based service recommendation system: A JPEG-coefficient RFs approach. IEEE Access. 2020;8:3308–18.

Cai X, Hu Z, Zhao P, Zhang W, Chen J. A hybrid recommendation system with many-objective evolutionary algorithm. Expert Syst Appl. 2020. https://doi.org/10.1016/j.eswa.2020.113648 .

Esteban A, Zafra A, Romero C. Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowledge-Based Syst. 2020;194:105385.

Mondal S, Basu A, Mukherjee N. Building a trust-based doctor recommendation system on top of multilayer graph database. J Biomed Inform. 2020;110:103549.

Dhelim S, Ning H, Aung N, Huang R, Ma J. Personality-aware product recommendation system based on user interests mining and metapath discovery. IEEE Trans Comput Soc Syst. 2021;8:86–98.

Bhalse N, Thakur R. Algorithm for movie recommendation system using collaborative filtering. Materials Today: Proceedings. 2021. https://doi.org/10.1016/j.matpr.2021.01.235 .

Ke G, Du HL, Chen YC. Cross-platform dynamic goods recommendation system based on reinforcement learning and social networks. Appl Soft Computing. 2021;104:107213.

Chen X, Liu D, Xiong Z, Zha ZJ. Learning and fusing multiple user interest representations for micro-video and movie recommendations. IEEE Trans Multimedia. 2021;23:484–96.

Afolabi AO, Toivanen P. Integration of recommendation systems into connected health for effective management of chronic diseases. IEEE Access. 2019;7:49201–11.

He M, Wang B, Du X. HI2Rec: Exploring knowledge in heterogeneous information for movie recommendation. IEEE Access. 2019;7:30276–84.

Bobadilla J, Serradilla F, Hernando A. Collaborative filtering adapted to recommender systems of e-learning. Knowl-Based Syst. 2009;22:261–5.

Russell S, Yoon V. Applications of wavelet data reduction in a recommender system. Expert Syst Appl. 2008;34:2316–25.

Campos LM, Fernández-Luna JM, Huete JF. A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets Syst. 2008;159:1554–76.

Funk M, Rozinat A, Karapanos E, Medeiros AKA, Koca A. In situ evaluation of recommender systems: Framework and instrumentation. Int J Hum Comput Stud. 2010;68:525–47.

Porcel C, Moreno JM, Herrera-Viedma E. A multi-disciplinar recommender system to advice research resources in University Digital Libraries. Expert Syst Appl. 2009;36:12520–8.

Bobadilla J, Serradilla F, Bernal J. A new collaborative filtering metric that improves the behavior of recommender systems. Knowl-Based Syst. 2010;23:520–8.

Ochi P, Rao S, Takayama L, Nass C. Predictors of user perceptions of web recommender systems: How the basis for generating experience and search product recommendations affects user responses. Int J Hum Comput Stud. 2010;68:472–82.

Olmo FH, Gaudioso E. Evaluation of recommender systems: A new approach. Expert Syst Appl. 2008;35:790–804.

Zhen L, Huang GQ, Jiang Z. An inner-enterprise knowledge recommender system. Expert Syst Appl. 2010;37:1703–12.

Göksedef M, Gündüz-Öğüdücü S. Combination of web page recommender systems. Expert Syst Appl. 2010;37(4):2911–22.

Shao B, Wang D, Li T, Ogihara M. Music recommendation based on acoustic features and user access patterns. IEEE Trans Audio Speech Lang Process. 2009;17:1602–11.

Shin C, Woo W. Socially aware tv program recommender for multiple viewers. IEEE Trans Consum Electron. 2009;55:927–32.

Lopez-Carmona MA, Marsa-Maestre I, Perez JRV, Alcazar BA. Anegsys: An automated negotiation based recommender system for local e-marketplaces. IEEE Lat Am Trans. 2007;5:409–16.

Yap G, Tan A, Pang H. Discovering and exploiting causal dependencies for robust mobile context-aware recommenders. IEEE Trans Knowl Data Eng. 2007;19:977–92.

Meo PD, Quattrone G, Terracina G, Ursino D. An XML-based multiagent system for supporting online recruitment services. IEEE Trans Syst Man Cybern. 2007;37:464–80.

Khusro S, Ali Z, Ullah I. Recommender systems: Issues, challenges, and research opportunities. Inform Sci Appl. 2016. https://doi.org/10.1007/978-981-10-0557-2_112 .

Blanco-Fernandez Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, Lopez-Nores M. Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems. IEEE Trans Consum Electron. 2008;54:727–35.

Isinkaye FO, Folajimi YO, Ojokoh BA. Recommendation systems: Principles, methods and evaluation. Egyptian Inform J. 2015;16:261–73.

Yoshii K, Goto M, Komatani K, Ogata T, Okuno HG. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Trans Audio Speech Lang Process. 2008;16:435–47.

Wei YZ, Moreau L, Jennings NR. Learning users’ interests by quality classification in market-based recommender systems. IEEE Trans Knowl Data Eng. 2005;17:1678–88.

Bjelica M. Towards TV recommender system: experiments with user modeling. IEEE Trans Consum Electron. 2010;56:1763–9.

Setten MV, Veenstra M, Nijholt A, Dijk BV. Goal-based structuring in recommender systems. Interact Comput. 2006;18:432–56.

Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng. 2005;17:734–49.

Symeonidis P, Nanopoulos A, Manolopoulos Y. Providing justifications in recommender systems. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 2009;38:1262–72.

Zhan J, Hsieh C, Wang I, Hsu T, Liau C, Wang D. Privacy preserving collaborative recommender systems. IEEE Trans Syst Man Cybernet. 2010;40:472–6.

Burke R. Hybrid recommender systems: survey and experiments. User Model User-Adap Inter. 2002;12:331–70.

Article   MATH   Google Scholar  

Gunes I, Kaleli C, Bilge A, Polat H. Shilling attacks against recommender systems: a comprehensive survey. Artif Intell Rev. 2012;42:767–99.

Park DH, Kim HK, Choi IY, Kim JK. A literature review and classification of recommender systems research. Expert Syst Appl. 2012;39:10059–72.

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Ask your doctor about meeting with a Registered Dietitian with special training in kidney disease.

A dietitian can:

• Teach you to make the best food choices based on your lifestyle and lab tests
• Help to make changes in your diet to help you better control diabetes and high blood pressure
• Help you to keep your kidney disease from getting worse

Meeting with a dietitian is a service covered by Medicare and may also be covered by other types of insurance. You may need to call your insurance provider to see if your plan covers meeting with a dietitian.

Are you looking for nutrition guidance? Contact a CKD dietitian in your area. CKD Kidney Dietitian Directory

People with CKD may need to control the amount or type of one or more nutrients:

Eating the right amount or type of one or more of these nutrients may help control the buildup of waste and fluid in your blood. This means your kidneys do not have to work as hard to remove the extra waste and fluid. If your kidney disease gets worse, you may need to limit other nutrients as well. Your dietitian or healthcare provider will tell you if you need to do this based on your blood test results.

Sodium and your CKD Diet: How to Spice Up Your Cooking

Healthy kidneys control how much sodium is in your body. If your kidneys do not work well, too much sodium can cause fluid buildup, swelling, higher blood pressure, and strain on your heart. Your dietitian or healthcare provider can tell you the right amount of sodium you should have each day.

Potassium is important for muscle function, including the heart muscle. Too much or too little potassium in the blood can be very dangerous. The amount of potassium you need is based on how well your kidneys are working and your medications. Your dietitian or healthcare provider can tell you about foods with potassium and the right amount for you to eat each day.

Potassium from animal foods is absorbed at a higher rate than potassium from plants. Elevated blood sugars can also increase potassium levels. Some high-potassium foods include milk, meat, bananas, oranges and orange juice, cantaloupe, dried fruits, winter squash and salt substitutes.

Phosphorus and Your Diet

As kidney function gets lower, extra phosphorus can start building up in the blood. High phosphorus levels can cause bones to get weaker. Your dietitian or healthcare provider can tell you if you need to limit goods that are high in phosphorus.

High phosphorus foods include dairy products, organ meats, colas, deli meats, and processed foods with phosphate additives. 

Avoid processed foods that have additives with PHOS like disodium PHOSphate. You can find these additives in the ingredients list on food labels.

Foods that are good sources of calcium are often high in phosphorus. Your dietitian or healthcare provider will tell you if you need to limit calcium. Before taking any over-the-counter vitamin D or calcium supplements, talk to your healthcare provider.

Your body needs protein to help build muscle, repair tissue, and fight infection. Plant-based protein like beans and nuts are easier on the kidneys than animal proteins like meat and dairy products.  This is because plant proteins produce less acid in the body than meat proteins. More acid in body can damage the kidneys.

Having very large amounts of protein, especially in the form of unnecessary supplements, can be stressful on the kidneys. But if protein intake is too low, you can lose muscle mass, become weak, and increase the body’s acid load, so it is important to eat the right amount each day. The amount of protein you need is based on:

• Your weight
• Your age – growing children and adolescents, and older people, need more protein
• The amount of protein in your urine, and depending on your type of kidney disease
• Your dietitian or healthcare provider can tell you how much protein you should eat.

The Dos and Don'ts of Fluid Management in Chronic Kidney Disease

Most people in the early stages of kidney disease do not need to limit how much fluid they drink. If you do not know your stage of kidney disease, ask your healthcare provider. If your kidney disease worsens, your dietitian or healthcare provider can let you know if you need to limit fluids and how much to drink each day.  Fluids may need to be limited based on how much urine you make each day, and if fluid build-up in your body is causing swelling or stress on your heart or lungs.

Fluids include any food that is liquid at room temperature such as beverages, soups, gelatin, frozen desserts, and ice.

Every person is different in terms of the calories they need each day. Calories are like fuel. If you don’t eat enough, your body will take protein from the muscles for energy. It’s important to make sure you get the right number of calories. If you don’t, you may get weak and damage your kidneys. Eating the right number of calories will:

• Help you stay at a healthy weight
• Give you the energy to do your daily tasks
• Help your body use the protein in food to build muscle and tissues

Too many calories can cause extra weight gain, which can burden the kidneys. If you are overweight, some weight loss may be helpful. If weight loss is desired or you have diabetes, you should meet with a dietitian to set up a plan based on your kidney blood tests, current food choices, and daily activities.

Vitamins and Minerals in Chronic Kidney Disease

Most people get enough vitamins and minerals to stay healthy by eating a variety of foods each day. But kidney patients may need to limit some of these foods. If so, you may need to take special vitamin or mineral supplements, but only if a dietitian or healthcare provider tells you to. Some vitamins or mineral supplements may be harmful to people with kidney disease. You should check with your healthcare provider before taking any medications you can buy without a prescription. Some supplements may be harmful to people with kidney disease. Over the counter vitamin and mineral supplements may contain too much phosphorous and potassium. You should also avoid taking herbal supplements.

• What type of diet is best for my individual needs?
• Can you refer me to a registered dietitian nutritionist (RDN) who is an expert in kidney disease?
• If I need a vitamin and mineral supplement, can you prescribe one that is covered by Medicare or Medicaid?
• Can you check my blood to see if my acid level is too high?  If it’s too high, how can I change my diet to help lower the acid?
• Where can I find financial and other support in getting healthy foods and meals for people with CKD?
• Am I getting enough protein in my diet to maintain my muscle mass?
• Nutrition and Chronic Kidney Disease (Stages 1-5, Not on Dialysis): Getting the most out of the foods you eat
• Food Is Medicine Coalition List of Medically Tailored Meals Agencies (free meals for people with CKD)
• Treatment and Support
• Your Guide to Create a Balanced Kidney-Friendly Meal

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Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.)

Public involvement in research: assessing impact through a realist evaluation.

Chapter 9 conclusions and recommendations for future research.

• How well have we achieved our original aim and objectives?

The initially stated overarching aim of this research was to identify the contextual factors and mechanisms that are regularly associated with effective and cost-effective public involvement in research. While recognising the limitations of our analysis, we believe we have largely achieved this in our revised theory of public involvement in research set out in Chapter 8 . We have developed and tested this theory of public involvement in research in eight diverse case studies; this has highlighted important contextual factors, in particular PI leadership, which had not previously been prominent in the literature. We have identified how this critical contextual factor shapes key mechanisms of public involvement, including the identification of a senior lead for involvement, resource allocation for involvement and facilitation of research partners. These mechanisms then lead to specific outcomes in improving the quality of research, notably recruitment strategies and materials and data collection tools and methods. We have identified a ‘virtuous circle’ of feedback to research partners on their contribution leading to their improved confidence and motivation, which facilitates their continued contribution. Following feedback from the HS&DR Board on our original application we did not seek to assess the cost-effectiveness of different mechanisms of public involvement but we did cost the different types of public involvement as discussed in Chapter 7 . A key finding is that many research projects undercost public involvement.

In our original proposal we emphasised our desire to include case studies involving young people and families with children in the research process. We recruited two studies involving parents of young children aged under 5 years, and two projects involving ‘older’ young people in the 18- to 25-years age group. We recognise that in doing this we missed studies involving children and young people aged under 18 years; in principle we would have liked to have included studies involving such children and young people, but, given the resources at our disposal and the additional resource, ethical and governance issues this would have entailed, we regretfully concluded that this would not be feasible for our study. In terms of the four studies with parental and young persons’ involvement that we did include, we have not done a separate analysis of their data, but the themes emerging from those case studies were consistent with our other case studies and contributed to our overall analysis.

In terms of the initial objectives, we successfully recruited the sample of eight diverse case studies and collected and analysed data from them (objective 1). As intended, we identified the outcomes of involvement from multiple stakeholders‘ perspectives, although we did not get as many research partners‘ perspectives as we would have liked – see limitations below (objective 2). It was more difficult than expected to track the impact of public involvement from project inception through to completion (objective 3), as all of our projects turned out to have longer time scales than our own. Even to track involvement over a stage of a case study research project proved difficult, as the research usually did not fall into neatly staged time periods and one study had no involvement activity over the study period.

Nevertheless, we were able to track seven of the eight case studies prospectively and in real time over time periods of up to 9 months, giving us an unusual window on involvement processes that have previously mainly been observed retrospectively. We were successful in comparing the contextual factors, mechanisms and outcomes associated with public involvement from different stakeholders‘ perspectives and costing the different mechanisms for public involvement (objective 4). We only partly achieved our final objective of undertaking a consensus exercise among stakeholders to assess the merits of the realist evaluation approach and our approach to the measurement and valuation of economic costs of public involvement in research (objective 5). A final consensus event was held, where very useful discussion and amendment of our theory of public involvement took place, and the economic approach was discussed and helpfully critiqued by participants. However, as our earlier discussions developed more fully than expected, we decided to let them continue rather than interrupt them in order to run the final exercise to assess the merits of the realist evaluation approach. We did, however, test our analysis with all our case study participants by sending a draft of this final report for comment. We received a number of helpful comments and corrections but no disagreement with our overall analysis.

• What were the limitations of our study?

Realist evaluation is a relatively new approach and we recognise that there were a number of limitations to our study. We sought to follow the approach recommended by Pawson, but we acknowledge that we were not always able to do so. In particular, our theory of public involvement in research evolved over time and initially was not as tightly framed in terms of a testable hypothesis as Pawson recommends. In his latest book Pawson strongly recommends that outcomes should be measured with quantitative data, 17 but we did not do so; we were not aware of the existence of quantitative data or tools that would enable us to collect such data to answer our research questions. Even in terms of qualitative data, we did not capture as much information on outcomes as we initially envisaged. There were several reasons for this. The most important was that capturing outcomes in public involvement is easier the more operational the focus of involvement, and more difficult the more strategic the involvement. Thus, it was relatively easy to see the impact of a patient panel on the redesign of a recruitment leaflet but harder to capture the impact of research partners in a multidisciplinary team discussion of research design.

We also found it was sometimes more difficult to engage research partners as participants in our research than researchers or research managers. On reflection this is not surprising. Research partners are generally motivated to take part in research relevant to their lived experience of a health condition or situation, whereas our research was quite detached from their lived experience; in addition people had many constraints on their time, so getting involved in our research as well as their own was likely to be a burden too far for some. Researchers clearly also face significant time pressures but they had a more direct interest in our research, as they are obliged to engage with public involvement to satisfy research funders such as the NIHR. Moreover, researchers were being paid by their employers for their time during interviews with us, while research partners were not paid by us and usually not paid by their research teams. Whatever the reasons, we had less response from research partners than researchers or research managers, particularly for the third round of data collection; thus we have fewer data on outcomes from research partners‘ perspectives and we need to be aware of a possible selection bias towards more engaged research partners. Such a bias could have implications for our findings; for example payment might have been a more important motivating factor for less engaged advisory group members.

There were a number of practical difficulties we encountered. One challenge was when to recruit the case studies. We recruited four of our eight case studies prior to the full application, but this was more than 1 year before our project started and 15 months or more before data collection began. In this intervening period, we found that the time scales of some of the case studies were no longer ideal for our project and we faced the choice of whether to continue with them, although this timing was not ideal, or seek at a late moment to recruit alternative ones. One of our case studies ultimately undertook no involvement activity over the study period, so we obtained fewer data from it, and it contributed relatively little to our analysis. Similarly, one of the four case studies we recruited later experienced some delays itself in beginning and so we had a more limited period for data collection than initially envisaged. Research governance approvals took much longer than expected, particularly as we had to take three of our research partners, who were going to collect data within NHS projects, through the research passport process, which essentially truncated our data collection period from 1 year to 9 months. Even if we had had the full year initially envisaged for data collection, our conclusion with hindsight was that this was insufficiently long. To compare initial plans and intentions for involvement with the reality of what actually happened required a longer time period than a year for most of our case studies.

In the light of the importance we have placed on the commitment of PIs, there is an issue of potential selection bias in the recruitment of our sample. As our sampling strategy explicitly involved a networking approach to PIs of projects where we thought some significant public involvement was taking place, we were likely (as we did) to recruit enthusiasts and, at worst, those non-committed who were at least open to the potential value of public involvement. There were, unsurprisingly, no highly sceptical PIs in our sample. We have no data therefore on how public involvement may work in research where the PI is sceptical but may feel compelled to undertake involvement because of funder requirements or other factors.

• What would we do differently next time?

If we were to design this study again, there are a number of changes we would make. Most importantly we would go for a longer time period to be able to capture involvement through the whole research process from initial design through to dissemination. We would seek to recruit far more potential case studies in principle, so that we had greater choice of which to proceed with once our study began in earnest. We would include case studies from the application stage to capture the important early involvement of research partners in the initial design period. It might be preferable to research a smaller number of case studies, allowing a more in-depth ethnographic approach. Although challenging, it would be very informative to seek to sample sceptical PIs. This might require a brief screening exercise of a larger group of PIs on their attitudes to and experience of public involvement.

The economic evaluation was challenging in a number of ways, particularly in seeking to obtain completed resource logs from case study research partners. Having a 2-week data collection period was also problematic in a field such as public involvement, where activity may be very episodic and infrequent. Thus, collecting economic data alongside other case study data in a more integrated way, and particularly with interviews and more ethnographic observation of case study activities, might be advantageous. The new budgeting tool developed by INVOLVE and the MHRN may provide a useful resource for future economic evaluations. 23

We have learned much from the involvement of research partners in our research team and, although many aspects of our approach worked well, there are some things we would do differently in future. Even though we included substantial resources for research partner involvement in all aspects of our study, we underestimated how time-consuming such full involvement would be. We were perhaps overambitious in trying to ensure such full involvement with the number of research partners and the number and complexity of the case studies. We were also perhaps naive in expecting all the research partners to play the same role in the team; different research partners came with different experiences and skills, and, like most of our case studies, we might have been better to be less prescriptive and allow the roles to develop more organically within the project.

• Implications for research practice and funding

If one of the objectives of R&D policy is to increase the extent and effectiveness of public involvement in research, then a key implication of this research is the importance of influencing PIs to value public involvement in research or to delegate to other senior colleagues in leading on involvement in their research. Training is unlikely to be the key mechanism here; senior researchers are much more likely to be influenced by peers or by their personal experience of the benefits of public involvement. Early career researchers may be shaped by training but again peer learning and culture may be more influential. For those researchers sceptical or agnostic about public involvement, the requirement of funders is a key factor that is likely to make them engage with the involvement agenda. Therefore, funders need to scrutinise the track record of research teams on public involvement to ascertain whether there is any evidence of commitment or leadership on involvement.

One of the findings of the economic analysis was that PIs have consistently underestimated the costs of public involvement in their grant applications. Clearly the field will benefit from the guidance and budgeting tool recently disseminated by MHRN and INVOLVE. It was also notable that there was a degree of variation in the real costs of public involvement and that effective involvement is not necessarily costly. Different models of involvement incur different costs and researchers need to be made aware of the costs and benefits of these different options.

One methodological lesson we learned was the impact that conducting this research had on some participants’ reflection on the impact of public involvement. Particularly for research staff, the questions we asked sometimes made them reflect upon what they were doing and change aspects of their approach to involvement. Thus, the more the NIHR and other funders can build reporting, audit and other forms of evaluation on the impact of public involvement directly into their processes with PIs, the more likely such questioning might stimulate similar reflection.

• Recommendations for further research

There are a number of gaps in our knowledge around public involvement in research that follow from our findings, and would benefit from further research, including realist evaluation to extend and further test the theory we have developed here:

• In-depth exploration of how PIs become committed to public involvement and how to influence agnostic or sceptical PIs would be very helpful. Further research might compare, for example, training with peer-influencing strategies in engendering PI commitment. Research could explore the leadership role of other research team members, including research partners, and how collective leadership might support effective public involvement.
• More methodological work is needed on how to robustly capture the impact and outcomes of public involvement in research (building as well on the PiiAF work of Popay et al. 51 ), including further economic analysis and exploration of impact when research partners are integral to research teams.
• Research to develop approaches and carry out a full cost–benefit analysis of public involvement in research would be beneficial. Although methodologically challenging, it would be very useful to conduct some longer-term studies which sought to quantify the impact of public involvement on such key indicators as participant recruitment and retention in clinical trials.
• It would also be helpful to capture qualitatively the experiences and perspectives of research partners who have had mixed or negative experiences, since they may be less likely than enthusiasts to volunteer to participate in studies of involvement in research such as ours. Similarly, further research might explore the (relatively rare) experiences of marginalised and seldom-heard groups involved in research.
• Payment for public involvement in research remains a contested issue with strongly held positions for and against; it would be helpful to further explore the value research partners and researchers place on payment and its effectiveness for enhancing involvement in and impact on research.
• A final relatively narrow but important question that we identified after data collection had finished is: what is the impact of the long periods of relative non-involvement following initial periods of more intense involvement for research partners in some types of research, particularly clinical trials?

Included under terms of UK Non-commercial Government License .

• Cite this Page Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.) Chapter 9, Conclusions and recommendations for future research.
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Mrna vaccine technology wins 2023 nobel prize, highlighting importance of rna research.

On Monday, the 2023 Nobel Prize in Physiology or Medicine was awarded to Katalin Karikó and Drew Weissman for their contributions to the field of mRNA vaccines – pioneering work that underscores the increasingly popular field of RNA research.

Their work focused on discovery of the nucleoside base modifications that enabled the unprecedented development of effective mRNA vaccines against COVID-19. This prestigious recognition highlights the transformative potential impact of RNA biology, an emerging application area that Oxford Nanopore is uniquely positioned to help advance.

With nanopore sequencing, scientists can analyse native RNA of any fragment length - including full length isoforms - and electronically analyse RNA modifications ‘at the source’.

It has been exciting to see many scientists investigate the biological implications of this feature. For instance, BASE researchers at the University of Queensland are using nanopore sequencing to optimise vaccine performance, and to reduce the time needed to measure mRNA vaccine quality attributes. Oxford Nanopore takes great pride in its unmatched position at the forefront of this rapidly developing industry.

Gordon Sanghera, CEO, Oxford Nanopore Technologies, commented: “Congratulations to Katalin Karikó and Drew Weissman for their remarkable achievement in advancing the field of mRNA vaccines. As we look to the future of vaccine development, RNA modification is an area of exciting potential and one the Oxford Nanopore scientific community is increasingly focused on. With the only technology currently available to accurately read native, full-length RNA, Oxford Nanopore is proud to be in a position to support research and development in this fast-moving space, helping unleash new potential for RNA-based therapies and vaccines.”

Libby Snell, Principal Scientist, Sample Technology, Oxford Nanopore Technologies, commented: “It is wonderful to see that the work of Katalin Karikó and Drew Weissman on RNA modifications has been recognised with such a highly deserved honour. We congratulate them for all their contributions leading to this achievement. This is undoubtedly a very exciting time for RNA science, especially within the field of RNA therapeutics and mRNA vaccines. We are excited to continue developing Oxford Nanopore’s unique ability to accurately read modified bases using long-read direct RNA sequencing – and support RNA research and biopharma manufacturing as these burgeoning fields advance.”

Further information:

Oxford Nanopore’s direct RNA sequencing kit (SQK-RNA004) is sensitive to a variety of RNA modifications and stands alone as the sole sequencing platform capable of accurately reading native full-length modified RNAs. More information on this kit can be found here.

• Published on: October 2 2023

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• HAIG: Buy-Sell Q&A: As market softens, consider these hard truths from industry experts
• HYUNDAI: We’re not waiting for the future. We’re creating it.
• Haig Partners: Buy-Sell Q&A: Where the Automotive M&A Market is heading
• Haig: BUY-SELL Q&A: How To Set a Record: Anatomy Of a Deal That Delivered
• Haig: BUY-SELL Q&A: Value Of Buying vs. Benefit Of Selling Dealerships Today
• Haig: Buy-Sell Q&A | Advisors add value to sales process
• Haig: Buy-Sell Q&A | Navigating partial dealership sales
• Haig: Buy-Sell Q&A | growth drives value
• Haig: Buy-Sell Q&A: Future-proofing your dealership
• Haig: Buy-Sell Q&A: What is the value of an M&A advisor?
• Hesai Technology: Lidar technology continues to gain momentum
• Hyundai: 2023 year in review: Milestones and future visions
• Hyundai: Great partnerships lead to great success
• Hyundai: Hyundai's vision for the progress of humanity
• Hyundai: You’re going to need a bigger cart: Hyundai unveils new Amazon partnership
• JM&A Group, Easycare, Assurant: Unlocking new opportunities in F&I with digital retailing
• KEYSIGHT TECHNOLOGIES AND MAHLE INDUSTRIES INC.: Plugging Away on Batteries Without Getting Jolted
• Kerrigan Advisors: Interview with Baxter Auto Group
• Kerrigan Advisors: Interview with Hitchcock Automotive
• Kerrigan Advisors: Interview with Keyes Motors
• Kerrigan: Leadership lessons and legacy: Kerrigan Advisors facilitates historic sale of Jim Koons Automotive to Asbury Automotive Group
• Kinsel Motors: A century-long legacy sold to Aoggett Automotive Group - Family’s perspective on the sale and the future of auto retail
• LUXOFT: How are software-defined vehicles disrupting the automotive industry?
• LendBuzz: Transforming auto lending beyond FICO: AI’s impact on expanding creditworthy customers
• Luxoft: See it, hear it, feel it: How driving assistants grab your attention
• NEXTEER AUTOMOTIVE AND ETAS INC: Software-defined vehicles will transform auto industry
• Nexteer and Etas: Software-defined vehicles will transform auto industry
• Nexteer: How megatrends like software & electrification are changing the auto industry & collaboration between OEMs & suppliers
• Nexteer; Exploring strategic automotive partnerships in the software-defined vehicle era
• OEC: How to understand what customers are saying when they’re not saying it
• PACE: Electrification Technology: Once the supply chain gets over the shock, EVs offer opportunities
• Pace Program Navigating advanced driver-assistance systems
• Phone Ninjas: 5 Reasons you need to use phone scripts for your dealership
• REYNOLDS & REYNOLDS: Creating great retail customer experiences
• REYNOLDS AND REYNOLDS: Are you ready for the FTC Safeguard Rule changes? Take this quiz to find out
• Reputation: How can dealers use consumer feedback data to foster growth and trust?
• Rey and Rey: Automation and the future of automotive retail
• Route One: Trends in vehicle buyer expectations
• RouteOne: Transitioning to true eContracting: Digital demands reshape automotive retail
• Routeone: Building a digital vehicle purchase path
• Routeone: Embracing Omnichannel: Enhancing the modern vehicle buying experience
• Routeone: The benefits of robust dealership reporting tools
• Routeone: Transforming automotive retail: Transitioning to true eContracting in a digital era
• Schneider Electric: Can smart factories deliver on the net-zero promise of EVs?
• Siemens: The impact of vehicle electrification and connectivity on electrical system design
• Solera: Unleashing the power of AI: How auto dealers can maximize revenue and efficiency
• Spectrum Reach Automotive: Multiscreen attribution insights that every dealer can benefit from
• Spectrum Reach: Is Your Advertising Reaching All Generations of Car Buyers?
• Spectrum Reach: Playing to win: How automotive dealers can capitalize on sports marketing
• Spectrum Reach: Use data and the power of multiscreen TV to drive more traffic
• Swiss Re: Insurers and car manufacturers: how to unlock the potential of true collaboration
• Syntax: Digital factory-as-a-service: The fast lane to digital transformation
• TRAXTION: Driving customer retention in fixed operations: Transparency is critical to retaining service customers
• Text2Drive: Digital retailing meets the service department
• Trimble: The future of autonomous vehicles speeds ahead
• Truist: Capitalize on the changing structure of auto retailing
• Truist: Prepare now for the next era of automotive retailing
• Tyler Technologies: A new national digital titling clearinghouse can accelerate vehicle purchases
• URBAN SCIENCE: Parts & Service: Building the Service Operation of the Future
• Urban Science: How to leverage data-driven analytics to drive dealership sales
• Urban Science: Promoting dealer-consumer alignment across continued industry evolution
• Vitu: E-Titling gains industry acceptance and is on track for one million titles processed
• Volta: Electric vehicles are here — Is your marketing department ready?
• Walbridge: Video | Walbridge Chairman on how automotive companies are navigating the electrification boom
• Western Digital: To Meet Consumer Demand, Automakers Must Double Down on Agile Development
• Western Digital: Understanding the diverse storage requirements for the vehicle of the future
• Wipro: From Autonomy to AI: Four ways auto ecosystem partners are changing the game
• Wipro: How generative AI becomes a catalyst in the softwarization of automobiles
• Wipro: How to address safety and security for software-defined vehicles
• Wipro: How today’s tech decisions drive tomorrow’s sales
• Wipro: Software Helps Address the Affordability Challenge
• Wipro: The Promise of Software-Defined Vehicles and the Cloud Car Ecosystem
• Wipro: Video | Wipro CTO on how OEM's are leveraging software-defined vehicle technology
• Wipro: Why Built-to-Order models are the future in the automotive Industry
• alumobility: How automakers can boost EV range and affordability: The future rides on aluminum
• Notarize: What to expect for the future of auto sales
• Seagate: Unlocking value from connected-car data
• Amazon Web Services: AI-powered supply chain decision-making
• Epic Games: Five ways in-car designs will change in the next five years
• Haig Partners: How are dealerships being valued today?
• Haig Partners: Dealership consolidation trends
• Haig Partners: Dealership valuation trends
• Haig Partners: Dealership succession planning
• Ally: Navigating the future of automotive retailing
• Google: How a century-old brand is transforming the auto industry
• Car Wars: Is a Rise in Service Leading to Poor Customer Satisfaction?
• Solera | DealerSocket: Four real-time integrations that can save your dealership time
• Solera | DealerSocket: Time for a new pre-owned pricing tactic
• Kerrigan Advisors: Blue Sky Update Q4 2021
• Qualcomm: Trading multi-year design cycles for on-demand features and experiences
• Twitter: EV adoption is happening...in an unexpected place
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