mlsanityData to 7 Oct 2026

User-oriented Fairness in Recommendation

arXiv 2104.10671 ↗First posted 21 Apr 2021cs.IR cs.AI cs.HCcited by 75

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Abstract (arXiv)

As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and solve the unfairness issues in recommendation scenarios. In this paper, we address the unfairness problem in recommender systems from the user perspective. We group users into advantaged and disadvantaged groups according to their level of activity, and conduct experiments to show that current recommender systems will behave unfairly between two groups of users. Specifically, the advantaged users (active) who only account for a small proportion in data enjoy much higher recommendation quality than those disadvantaged users (inactive). Such bias can also affect the overall performance since the disadvantaged users are the majority. To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics. The experiments we conducted on several real-world datasets with various recommendation algorithms show that our approach can not only improve group fairness of users in recommender systems, but also achieve better overall recommendation performance.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 21 Apr 2021.

Builds on

25 of 46 references.

References · 25

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2017Neural Collaborative Filteringcited by 1,108
  3. 2018A Reductions Approach to Fair Classificationcited by 585
  4. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  5. 2018Fairness of Exposure in Rankingscited by 189
  6. 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 181
  7. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  8. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  9. 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
  10. 2019Managing Popularity Bias in Recommender Systems with Personalized Re-rankingcited by 81
  11. 2017Ranking with Fairness Constraintscited by 78
  12. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
13 more
  1. 2021Towards Long-term Fairness in Recommendationcited by 77
  2. 2019Policy Learning for Fairness in Rankingcited by 74
  3. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  4. 2017Multisided Fairness for Recommendationcited by 69
  5. 2016Satisfying Real-world Goals with Dataset Constraintscited by 68
  6. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  7. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  8. 2018Two-Player Games for Efficient Non-Convex Constrained Optimizationcited by 55
  9. 2020Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithmscited by 24
  10. 2019FairSearch: A Tool For Fairness in Ranked Search Resultscited by 22
  11. 2018User Fairness in Recommender Systemscited by 20
  12. 2019Multi-stakeholder Recommendation and its Connection to Multi-sided Fairnesscited by 19
  13. 2016Graph Clustering Bandits for Recommendationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 16; 2023: 22; 2024: 20; 2025: 11; 2026: 3.

75 papers.

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  1. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  2. 2022Fairness in Graph Mining: A Surveycited by 76
  3. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  4. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  5. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  6. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  7. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  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. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  11. 2023Ensuring User-side Fairness in Dynamic Recommender Systemscited by 27
  12. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
38 more
  1. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  2. 2025iAgent: LLM Agent as a Shield between User and Recommender Systemscited by 23
  3. 2022Comprehensive Fair Meta-learned Recommender Systemcited by 18
  4. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  5. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  6. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  7. 2023A Topological Perspective on Demystifying GNN-Based Link Prediction Performancecited by 11
  8. 2022RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systemscited by 10
  9. 2024Fairness Definitions in Language Models Explainedcited by 10
  10. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9
  11. 2025Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policiescited by 9
  12. 2023Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspectivecited by 8
  13. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  14. 2024FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learningcited by 8
  15. 2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influencecited by 6
  16. 2022Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioningcited by 4
  17. 2023When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?cited by 4
  18. 2024A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender Systemcited by 4
  19. 2024Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosiscited by 4
  20. 2023FairRec: Fairness Testing for Deep Recommender Systemscited by 3
  21. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
  22. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  23. 2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
  24. 2021Problem Learning: Towards the Free Will of Machinescited by 2
  25. 2022Analyzing the Effect of Sampling in GNNs on Individual Fairnesscited by 2
  26. 2022Equal Experience in Recommender Systemscited by 2
  27. 2023Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Searchcited by 2
  28. 2023FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Softwarecited by 2
  29. 2023Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbationscited by 2
  30. 2023Fairness Vs. Personalization: Towards Equity in Epistemic Utilitycited by 2
  31. 2024AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gapscited by 2
  32. 2024Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approachcited by 2
  33. 2024Perceptual Fairness in Image Restorationcited by 2
  34. 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injectionscited by 2
  35. 2024Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographicscited by 2
  36. 2024DifFaiRec: Generative Fair Recommender with Conditional Diffusion Modelcited by 2
  37. 2024Towards a Fairer Non-negative Matrix Factorizationcited by 2
  38. 2025Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendationscited by 2

50 most cited of 75 All 75 (JSON)