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Multisided Fairness for Recommendation

arXiv 1707.00093 ↗First posted 1 Jul 2017cs.CY cs.IRcited by 70

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Abstract (arXiv)

Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.

Key facts. Cited by 70 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jul 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 14; 2022: 14; 2023: 12; 2024: 6; 2025: 14; 2026: 10.

70 papers.

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  1. 2021User-oriented Fairness in Recommendationcited by 75
  2. 2021Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Featurescited by 45
  3. 2023A Survey on Popularity Bias in Recommender Systemscited by 43
  4. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  5. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  6. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  7. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  8. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  9. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  10. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  11. 2021Online certification of preference-based fairness for personalized recommender systemscited by 22
  12. 2022Joint Multisided Exposure Fairness for Recommendationcited by 22
38 more
  1. 2024The Impossibility of Fair LLMscited by 22
  2. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  3. 2021Achieving Counterfactual Fairness for Causal Banditcited by 15
  4. 2021Estimation of Fair Ranking Metrics with Incomplete Judgmentscited by 14
  5. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  6. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  7. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  8. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  9. 2021Fairness and Transparency in Recommendation: The Users' Perspectivecited by 10
  10. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9
  11. 2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
  12. 2021Fairness Through Regularization for Learning to Rankcited by 8
  13. 2021Towards Fair Recommendation in Two-Sided Platformscited by 8
  14. 2023Learning with Exposure Constraints in Recommendation Systemscited by 8
  15. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  16. 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
  17. 2023DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervisioncited by 6
  18. 2025Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Settingcited by 6
  19. 2026Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challengescited by 6
  20. 2022Fast online ranking with fairness of exposurecited by 5
  21. 2022Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluationscited by 5
  22. 2024Achieving Fairness Across Local and Global Models in Federated Learningcited by 5
  23. 2022Unintended Bias in Language Model-driven Conversational Recommendationcited by 4
  24. 2023Fairness in Ranking under Disparate Uncertaintycited by 4
  25. 2021DiRe Committee : Diversity and Representation Constraints in Multiwinner Electionscited by 3
  26. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
  27. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  28. 2025Advances in Set Function Learning: A Survey of Techniques and Applicationscited by 3
  29. 2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
  30. 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
  31. 2022Who Pays? Personalization, Bossiness and the Cost of Fairnesscited by 2
  32. 2022Equal Experience in Recommender Systemscited by 2
  33. 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
  34. 2023Trading-off price for data quality to achieve fair online allocationcited by 2
  35. 2024Overhead-free User-side Recommender Systemscited by 2
  36. 2026Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankerscited by 2
  37. 2023In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systemscited by 1
  38. 2023Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUFcited by 1

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