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The Unfairness of Popularity Bias in Recommendation

arXiv 1907.13286 ↗First posted 31 Jul 2019cs.IRcited by 68

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Abstract (arXiv)

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or otherwise the overall catalog coverage. In this paper, however, we look at this problem from the users' perspective: we want to see how popularity bias causes the recommendations to deviate from what the user expects to get from the recommender system. We define three different groups of users according to their interest in popular items (Niche, Diverse and Blockbuster-focused) and show the impact of popularity bias on the users in each group. Our experimental results on a movie dataset show that in many recommendation algorithms the recommendations the users get are extremely concentrated on popular items even if a user is interested in long-tail and non-popular items showing an extreme bias disparity.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2019.

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Citing papers per year: 2021: 8; 2022: 15; 2023: 22; 2024: 11; 2025: 6; 2026: 6.

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  1. 2023Recommender Systems with Generative Retrievalcited by 191
  2. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  3. 2022Fairness in Graph Mining: A Surveycited by 76
  4. 2021User-oriented Fairness in Recommendationcited by 75
  5. 2024Large Language Models Empowered Personalized Web Agentscited by 64
  6. 2023A Survey on Popularity Bias in Recommender Systemscited by 42
  7. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  8. 2021PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotioncited by 37
  9. 2021Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLPcited by 30
  10. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  11. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  12. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
38 more
  1. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  2. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 25
  3. 2023Graph Collaborative Signals Denoising and Augmentation for Recommendationcited by 16
  4. 2024Agentic Feedback Loop Modeling Improves Recommendation and User Simulationcited by 16
  5. 2022The Unfairness of Popularity Bias in Book Recommendationcited by 14
  6. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  7. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  8. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  9. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  10. 2023Robust Collaborative Filtering to Popularity Distribution Shiftcited by 12
  11. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  12. 2023Text Matching Improves Sequential Recommendation by Reducing Popularity Biasescited by 10
  13. 2024Fairness Definitions in Language Models Explainedcited by 10
  14. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9
  15. 2022Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systemscited by 8
  16. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  17. 2023Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Modelscited by 7
  18. 2024Heterophily-Aware Fair Recommendation using Graph Convolutional Networkscited by 7
  19. 2022Fast online ranking with fairness of exposurecited by 5
  20. 2023Test Time Embedding Normalization for Popularity Bias Mitigationcited by 4
  21. 2024Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Modelscited by 4
  22. 2021Algorithms are not neutral: Bias in collaborative filteringcited by 3
  23. 2022Metricizing the Euclidean Space towards Desired Distance Relations in Point Cloudscited by 3
  24. 2023Metrics for popularity bias in dynamic recommender systemscited by 3
  25. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  26. 2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
  27. 2023Data augmentation and explainability for bias discovery and mitigation in deep learningcited by 2
  28. 2023A survey on bias in machine learning researchcited by 2
  29. 2024Algorithmic Fairness: A Tolerance Perspectivecited by 2
  30. 2025Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systemscited by 2
  31. 2025Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systemscited by 2
  32. 2022Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Servicescited by 1
  33. 2022Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systemscited by 1
  34. 2023Exploring and Exploiting Data Heterogeneity in Recommendationcited by 1
  35. 2023Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendationcited by 1
  36. 2024The Importance of Causality in Decision Making: A Perspective on Recommender Systemscited by 1
  37. 2025Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designscited by 1
  38. 2026Bridging Semantic Understanding and Popularity Bias with LLMscited by 1

50 most cited of 68 All 68 (JSON)