mlsanityData to 7 Oct 2026

Fairness in Recommendation Ranking through Pairwise Comparisons

arXiv 1903.00780 ↗First posted 2 Mar 2019cs.CY cs.AI cs.IRcited by 127

Not on the map. Why?

Abstract (arXiv)

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks, how can we quantify them, and how should we address them? In this paper we offer a set of novel metrics for evaluating algorithmic fairness concerns in recommender systems. In particular we show how measuring fairness based on pairwise comparisons from randomized experiments provides a tractable means to reason about fairness in rankings from recommender systems. Building on this metric, we offer a new regularizer to encourage improving this metric during model training and thus improve fairness in the resulting rankings. We apply this pairwise regularization to a large-scale, production recommender system and show that we are able to significantly improve the system's pairwise fairness.

Key facts. Cited by 127 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 29; 2023: 33; 2024: 15; 2025: 16; 2026: 10.

127 papers.

Sort:

By area:

  1. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  2. 2021Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Searchcited by 90
  3. 2021User-oriented Fairness in Recommendationcited by 75
  4. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  5. 2021Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Featurescited by 45
  6. 2021Fairness of Exposure in Stochastic Banditscited by 42
  7. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  8. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  9. 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
  10. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  11. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
  12. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
38 more
  1. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  2. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  3. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  4. 2021Fairness in Ranking under Uncertaintycited by 27
  5. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  6. 2023Fair Machine Unlearning: Data Removal while Mitigating Disparitiescited by 25
  7. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  8. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  9. 2021When Fair Ranking Meets Uncertain Inferencecited by 22
  10. 2021Measuring Recommender System Effects with Simulated Userscited by 20
  11. 2023Mitigating Test-Time Bias for Fair Image Retrievalcited by 20
  12. 2021Lagrangian Inference for Ranking Problemscited by 19
  13. 2022Comprehensive Fair Meta-learned Recommender Systemcited by 18
  14. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  15. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  16. 2024Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankerscited by 17
  17. 2022Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcarecited by 16
  18. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  19. 2021Estimation of Fair Ranking Metrics with Incomplete Judgmentscited by 14
  20. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  21. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  22. 2023Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Imagescited by 13
  23. 2024PairEval: Open-domain Dialogue Evaluation with Pairwise Comparisoncited by 11
  24. 2021Fairness and Transparency in Recommendation: The Users' Perspectivecited by 10
  25. 2022Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networkscited by 10
  26. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  27. 2024Fairness Definitions in Language Models Explainedcited by 10
  28. 2021Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilitiescited by 9
  29. 2021Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approachcited by 9
  30. 2022FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddingscited by 9
  31. 2022Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraintscited by 9
  32. 2021Fairness Through Regularization for Learning to Rankcited by 8
  33. 2022Cross Pairwise Ranking for Unbiased Item Recommendationcited by 8
  34. 2023Learning with Exposure Constraints in Recommendation Systemscited by 8
  35. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  36. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  37. 2024Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMscited by 7
  38. 2021Pairwise Fairness for Ordinal Regressioncited by 6

50 most cited of 127 All 127 (JSON)