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Managing Popularity Bias in Recommender Systems with Personalized Re-ranking

arXiv 1901.07555 ↗First posted 22 Jan 2019cs.IRcited by 81

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Abstract (arXiv)

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the `long tail' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 11; 2023: 18; 2024: 11; 2025: 12; 2026: 16.

81 papers.

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  1. 2021User-oriented Fairness in Recommendationcited by 75
  2. 2023A Survey on Popularity Bias in Recommender Systemscited by 42
  3. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  4. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  5. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  6. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  7. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  8. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  9. 2021Measuring Recommender System Effects with Simulated Userscited by 20
  10. 2023Evaluating ChatGPT as a Recommender System: A Rigorous Approachcited by 20
  11. 2022The Unfairness of Popularity Bias in Book Recommendationcited by 14
  12. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
38 more
  1. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  2. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9
  3. 2021Towards Fair Recommendation in Two-Sided Platformscited by 8
  4. 2022Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systemscited by 8
  5. 2022Cross Pairwise Ranking for Unbiased Item Recommendationcited by 8
  6. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  7. 2021Reenvisioning Collaborative Filtering vs Matrix Factorizationcited by 6
  8. 2022Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendationcited by 6
  9. 2024Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairnesscited by 6
  10. 2025Unbiased Collaborative Filtering with Fair Samplingcited by 6
  11. 2025Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systemscited by 6
  12. 2023Online Recommendations for Agents with Discounted Adaptive Preferencescited by 5
  13. 2024Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Modelscited by 4
  14. 2026Auditing Preferences for Brands and Cultures in LLMscited by 4
  15. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  16. 2024Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systemscited by 3
  17. 2021Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddingscited by 2
  18. 2021An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender Systemcited by 2
  19. 2021FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendationcited by 2
  20. 2023Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigationcited by 2
  21. 2023A survey on bias in machine learning researchcited by 2
  22. 2023Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discoverycited by 2
  23. 2023Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposalscited by 2
  24. 2024Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendationscited by 2
  25. 2025Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelinescited by 2
  26. 2021Learning Robust Recommender from Noisy Implicit Feedbackcited by 1
  27. 2022Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounderscited by 1
  28. 2023Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendationcited by 1
  29. 2023Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Samplingcited by 1
  30. 2023When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendationscited by 1
  31. 2023Seller-side Outcome Fairness in Online Marketplacescited by 1
  32. 2024A review of feature selection strategies utilizing graph data structures and knowledge graphscited by 1
  33. 2024A Unified Causal Framework for Auditing Recommender Systems for Ethical Concernscited by 1
  34. 2024The Importance of Causality in Decision Making: A Perspective on Recommender Systemscited by 1
  35. 2025Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendationcited by 1
  36. 2025Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluationcited by 1
  37. 2025Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterestcited by 1
  38. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1

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