mlsanityData to 7 Oct 2026

Intersectional Two-sided Fairness in Recommendation

arXiv 2402.02816 ↗First posted 5 Feb 2024cs.IR cs.CY cs.LGcited by 8

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Abstract (arXiv)

Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fairness, and two-sided fairness which considers both user and item fairness simultaneously. However, we argue that the intersectional two-sided unfairness may still exist even if the RS is two-sided fair, which is observed and shown by empirical studies on real-world data in this paper, and has not been well-studied previously. To mitigate this problem, we propose a novel approach called Intersectional Two-sided Fairness Recommendation (ITFR). Our method utilizes a sharpness-aware loss to perceive disadvantaged groups, and then uses collaborative loss balance to develop consistent distinguishing abilities for different intersectional groups. Additionally, predicted score normalization is leveraged to align positive predicted scores to fairly treat positives in different intersectional groups. Extensive experiments and analyses on three public datasets show that our proposed approach effectively alleviates the intersectional two-sided unfairness and consistently outperforms previous state-of-the-art methods.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

39 of 62 references.

References · 39

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,404
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  4. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  5. 2018Fairness of Exposure in Rankingscited by 189
  6. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  7. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  8. 2018An Intersectional Definition of Fairnesscited by 126
  9. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  10. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  11. 2022A Survey on the Fairness of Recommender Systemscited by 102
  12. 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
27 more
  1. 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
  2. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  3. 2021Towards Long-term Fairness in Recommendationcited by 77
  4. 2021User-oriented Fairness in Recommendationcited by 75
  5. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  6. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 67
  7. 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
  8. 2021Learning Fair Representations for Recommendation: A Graph-based Perspectivecited by 50
  9. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  10. 2018Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systemscited by 37
  11. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  12. 2022Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systemscited by 31
  13. 2022Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learningcited by 29
  14. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  15. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  16. 2021Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendationcited by 25
  17. 2021Focus on the Common Good: Group Distributional Robustness Followscited by 25
  18. 2019Addressing Marketing Bias in Product Recommendationscited by 23
  19. 2021Online certification of preference-based fairness for personalized recommender systemscited by 22
  20. 2022Joint Multisided Exposure Fairness for Recommendationcited by 22
  21. 2023Fairly Adaptive Negative Sampling for Recommendationscited by 22
  22. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  23. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  24. 2021Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Userscited by 11
  25. 2021Maximizing Marginal Fairness for Dynamic Learning to Rankcited by 10
  26. 2023P-MMF: Provider Max-min Fairness Re-ranking in Recommender Systemcited by 7
  27. 2023Measuring Item Global Residual Value for Fair Recommendationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 4.

8 papers.

  1. 2024Double Correction Framework for Denoising Recommendationcited by 19
  2. 2024Popularity-Aware Alignment and Contrast for Mitigating Popularity Biascited by 9
  3. 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
  4. 2024When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendationcited by 0
  5. 2026Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendationcited by 0
  6. 2026Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matchingcited by 0
  7. 2026Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systemscited by 0
  8. 2026Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrixcited by 0