mlsanityData to 7 Oct 2026

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

arXiv 2204.08085 ↗First posted 17 Apr 2022cs.IR cs.AIcited by 29

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Abstract (arXiv)

Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 17 Apr 2022.

Builds on

22 of 41 references.

References · 22

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Neural Collaborative Filteringcited by 1,108
  4. 2020Fairness in Machine Learning: A Surveycited by 369
  5. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  6. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  7. 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 181
  8. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  9. 2019Managing Popularity Bias in Recommender Systems with Personalized Re-rankingcited by 81
  10. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  11. 2021Towards Long-term Fairness in Recommendationcited by 77
  12. 2021User-oriented Fairness in Recommendationcited by 75
10 more
  1. 2017Multisided Fairness for Recommendationcited by 69
  2. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  3. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  4. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  5. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  6. 2020User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Marketscited by 23
  7. 2020Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systemscited by 15
  8. 2021Estimation of Fair Ranking Metrics with Incomplete Judgmentscited by 14
  9. 2022The Unfairness of Popularity Bias in Book Recommendationcited by 14
  10. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 6; 2024: 9; 2025: 6; 2026: 2.

29 papers.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2024Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attributioncited by 35
  3. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  4. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  5. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  6. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
  7. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  8. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  9. 2023Fairness of ChatGPT and the Role Of Explainable-Guided Promptscited by 13
  10. 2023Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentationcited by 11
  11. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  12. 2024Heterophily-Aware Fair Recommendation using Graph Convolutional Networkscited by 7
17 more
  1. 2024How Fair is Your Diffusion Recommender Model?cited by 5
  2. 2023The Role of Relevance in Fair Rankingcited by 3
  3. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
  4. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  5. 2024Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systemscited by 3
  6. 2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
  7. 2022Who Pays? Personalization, Bossiness and the Cost of Fairnesscited by 2
  8. 2023Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendationcited by 1
  9. 2024MOReGIn: Multi-Objective Recommendation at the Global and Individual Levelscited by 1
  10. 2025What's in a Query: Polarity-Aware Distribution-Based Fair Rankingcited by 1
  11. 2025A Reproducibility Study of Product-side Fairness in Bundle Recommendationcited by 1
  12. 2024Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commercecited by 0
  13. 2025Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systemscited by 0
  14. 2025Producer-Fairness in Sequential Bundle Recommendationcited by 0
  15. 2025Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrievalcited by 0
  16. 2026Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Rankingcited by 0
  17. 2026FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalizationcited by 0