mlsanityData to 7 Oct 2026

Beyond Parity: Fairness Objectives for Collaborative Filtering

arXiv 1705.08804 ↗First posted 24 May 2017cs.IR cs.AI cs.LGcited by 93

Not on the map. Why?

Abstract (arXiv)

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 21; 2023: 18; 2024: 13; 2025: 12; 2026: 10.

93 papers.

Sort:

By area:

  1. 2022Fairness in Graph Mining: A Surveycited by 76
  2. 2021User-oriented Fairness in Recommendationcited by 75
  3. 2021AutoDebias: Learning to Debias for Recommendationcited by 68
  4. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  5. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  6. 2024Gender Bias in Large Language Models across Multiple Languagescited by 35
  7. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  8. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  9. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  10. 2023Ensuring User-side Fairness in Dynamic Recommender Systemscited by 27
  11. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  12. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
38 more
  1. 2022Comprehensive Fair Meta-learned Recommender Systemcited by 18
  2. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  3. 2023No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendationcited by 18
  4. 2021InfoFair: Information-Theoretic Intersectional Fairnesscited by 15
  5. 2021Personalized Education in the AI Era: What to Expect Next?cited by 13
  6. 2021A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systemscited by 13
  7. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  8. 2022Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learningcited by 13
  9. 2021What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Modelscited by 12
  10. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  11. 2024Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patientscited by 11
  12. 2021Fairness and Transparency in Recommendation: The Users' Perspectivecited by 10
  13. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  14. 2021Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice modelscited by 10
  15. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  16. 2024Graph Fairness Learning under Distribution Shiftscited by 10
  17. 2022FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddingscited by 9
  18. 2022Group Meritocratic Fairness in Linear Contextual Banditscited by 9
  19. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  20. 2024FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learningcited by 8
  21. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  22. 2021An Online Riemannian PCA for Stochastic Canonical Correlation Analysiscited by 7
  23. 2023Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairnesscited by 7
  24. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  25. 2021Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environmentcited by 6
  26. 2025Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Settingcited by 6
  27. 2022Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencodercited by 5
  28. 2022Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluationscited by 5
  29. 2022Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustnesscited by 5
  30. 2022Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analyticscited by 5
  31. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  32. 2023Maximizing Submodular Functions for Recommendation in the Presence of Biasescited by 5
  33. 2021User Acceptance of Gender Stereotypes in Automated Career Recommendationscited by 4
  34. 2022Breaking Fair Binary Classification with Optimal Flipping Attackscited by 4
  35. 2024Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosiscited by 4
  36. 2025FROG: Fair Removal on Graphscited by 4
  37. 2023FairRec: Fairness Testing for Deep Recommender Systemscited by 3
  38. 2024User-Side Realizationcited by 3

50 most cited of 93 All 93 (JSON)