mlsanityData to 7 Oct 2026

A Survey on Popularity Bias in Recommender Systems

arXiv 2308.01118 ↗First posted 2 Aug 2023cs.IR cs.AI cs.LGcited by 42

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Abstract (arXiv)

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations todays recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey, therefore, includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss todays literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 52 of its 181 references that are in the map. First posted on arXiv 2 Aug 2023.

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52 of 181 references.

References · 52

  1. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 246
  2. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  3. 2021Causal Intervention for Leveraging Popularity Bias in Recommendationcited by 116
  4. 2022A Survey on the Fairness of Recommender Systemscited by 102
  5. 2020Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender Systemcited by 94
  6. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 89
  7. 2019Managing Popularity Bias in Recommender Systems with Personalized Re-rankingcited by 81
  8. 2019Putting Fairness Principles into Practice: Challenges, Metrics, and Improvementscited by 70
  9. 2017Multisided Fairness for Recommendationcited by 69
  10. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  11. 2019A Troubling Analysis of Reproducibility and Progress in Recommender Systems Researchcited by 65
  12. 2018Sequence-Aware Recommender Systemscited by 58
38 more
  1. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  2. 2023Debiased Contrastive Learning for Sequential Recommendationcited by 38
  3. 2021PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotioncited by 37
  4. 2020The Connection Between Popularity Bias, Calibration, and Fairness in Recommendationcited by 35
  5. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  6. 2019Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedbackcited by 28
  7. 2021Fairness in Information Access Systemscited by 27
  8. 2019The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Studycited by 24
  9. 2020Multi-sided Exposure Bias in Recommendationcited by 23
  10. 2021Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendationcited by 23
  11. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  12. 2019Empirical Analysis of Session-Based Recommendation Algorithmscited by 16
  13. 2020FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systemscited by 15
  14. 2021PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularitycited by 14
  15. 2022The Unfairness of Popularity Bias in Book Recommendationcited by 14
  16. 2020Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendationcited by 12
  17. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  18. 2013Personalized News Recommendation with Context Treescited by 11
  19. 2021Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Userscited by 11
  20. 2022Quantifying and Mitigating Popularity Bias in Conversational Recommender Systemscited by 11
  21. 2021It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamiccited by 10
  22. 2023How Graph Convolutions Amplify Popularity Bias for Recommendation?cited by 10
  23. 2020Exploring Longitudinal Effects of Session-based Recommendationscited by 9
  24. 2021Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?cited by 9
  25. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9
  26. 2022Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systemscited by 8
  27. 2022Cross Pairwise Ranking for Unbiased Item Recommendationcited by 8
  28. 2019Quantitative analysis of Matthew effect and sparsity problem of recommender systemscited by 7
  29. 2022Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigationscited by 7
  30. 2023Curse of "Low" Dimensionality in Recommender Systemscited by 7
  31. 2020Fair Multi-Stakeholder News Recommender System with Hypergraph rankingcited by 6
  32. 2020Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendationcited by 5
  33. 2020Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrievalcited by 4
  34. 2021Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulationcited by 4
  35. 2020Quantifying the Effects of Recommendation Systemscited by 3
  36. 2021Auditing the Biases Enacted by YouTube for Political Topics in Germanycited by 3
  37. 2020Alleviating the recommendation bias via rank aggregationcited by 2
  38. 2021An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender Systemcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 18; 2026: 18.

42 papers.

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  1. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 25
  2. 2024"Global is Good, Local is Bad?": Understanding Brand Bias in LLMscited by 13
  3. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10
  4. 2025In-context Ranking Preference Optimizationcited by 9
  5. 2025Unbiased Collaborative Filtering with Fair Samplingcited by 6
  6. 2026LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systemscited by 5
  7. 2024CSRec: Rethinking Sequential Recommendation from A Causal Perspectivecited by 4
  8. 2025Calibrated Recommendations with Contextual Banditscited by 3
  9. 2024Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matterscited by 2
  10. 2025On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspectivecited by 2
  11. 2026Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoringcited by 2
  12. 2023When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendationscited by 1
30 more
  1. 2025Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomycited by 1
  2. 2025Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detectioncited by 1
  3. 2025Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendationcited by 1
  4. 2025From Personalization to Prejudice: Bias and Discrimination in Memory-Enhanced AI Agents for Recruitmentcited by 1
  5. 2026Bridging Semantic Understanding and Popularity Bias with LLMscited by 1
  6. 2026Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Auditcited by 1
  7. 2026Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedbackcited by 1
  8. 2025The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendationcited by 0
  9. 2025Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Spacecited by 0
  10. 2025Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Modelscited by 0
  11. 2025Large Language Models Are Still Misled by Simple Bias Ensemblescited by 0
  12. 2025PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systemscited by 0
  13. 2025Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendationscited by 0
  14. 2025Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representationscited by 0
  15. 2025Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendationcited by 0
  16. 2025The Quest for Reliable Metrics of Responsible AIcited by 0
  17. 2025Extracting Interaction-Aware Monosemantic Concepts in Recommender Systemscited by 0
  18. 2026Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategiescited by 0
  19. 2026Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendationcited by 0
  20. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0
  21. 2026Aligning Recommendations with User Popularity Preferencescited by 0
  22. 2026Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendationcited by 0
  23. 2026ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendationcited by 0
  24. 2026The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenderscited by 0
  25. 2026The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agentscited by 0
  26. 2026Revealing Safety-Critical Scenarios for UTM via Transformercited by 0
  27. 2026LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendationcited by 0
  28. 2026Stay or Stray - A Dynamical Systems Viewpoint of Popularity Biascited by 0
  29. 2026When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Modelscited by 0
  30. 2026RouteRec: Behavior-Guided Sparse Routing for Sequential Recommendationcited by 0