mlsanityData to 7 Oct 2026

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

arXiv 2002.10764 ↗First posted 25 Feb 2020cs.AI cs.GTcited by 77

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Abstract (arXiv)

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2020.

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Citing papers per year: 2021: 18; 2022: 19; 2023: 15; 2024: 15; 2025: 6; 2026: 4.

77 papers.

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  1. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  2. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  3. 2022Fairness in Graph Mining: A Surveycited by 76
  4. 2021User-oriented Fairness in Recommendationcited by 75
  5. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  6. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  7. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  8. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  9. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  10. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  11. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  12. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
38 more
  1. 2021User Tampering in Reinforcement Learning Recommender Systemscited by 23
  2. 2025iAgent: LLM Agent as a Shield between User and Recommender Systemscited by 23
  3. 2021Online certification of preference-based fairness for personalized recommender systemscited by 22
  4. 2022Joint Multisided Exposure Fairness for Recommendationcited by 22
  5. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  6. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  7. 2021A Next Basket Recommendation Reality Checkcited by 15
  8. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  9. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  10. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  11. 2022Fair Ranking as Fair Division: Impact-Based Individual Fairness in Rankingcited by 13
  12. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  13. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  14. 2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
  15. 2021Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarizationcited by 8
  16. 2021Towards Fair Recommendation in Two-Sided Platformscited by 8
  17. 2023Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspectivecited by 8
  18. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  19. 2022Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individualcited by 7
  20. 2024Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systemscited by 7
  21. 2021Fairness for Robust Learning to Rankcited by 6
  22. 2021Two-Sided Matching Meets Fair Divisioncited by 5
  23. 2022Fast online ranking with fairness of exposurecited by 5
  24. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  25. 2025Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender Systemcited by 5
  26. 2022Unintended Bias in Language Model-driven Conversational Recommendationcited by 4
  27. 2022Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioningcited by 4
  28. 2021Subgroup Fairness in Two-Sided Marketscited by 3
  29. 2021DiRe Committee : Diversity and Representation Constraints in Multiwinner Electionscited by 3
  30. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
  31. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  32. 2024Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocationcited by 3
  33. 2024Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systemscited by 3
  34. 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
  35. 2022Who Pays? Personalization, Bossiness and the Cost of Fairnesscited by 2
  36. 2022Contextual bandits with concave rewards, and an application to fair rankingcited by 2
  37. 2022Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendationcited by 2
  38. 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2

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