mlsanityData to 7 Oct 2026

TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

arXiv 2104.09024 ↗First posted 19 Apr 2021cs.AIcited by 31

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Abstract (arXiv)

At present, most research on the fairness of recommender systems is conducted either from the perspective of customers or from the perspective of product (or service) providers. However, such a practice ignores the fact that when fairness is guaranteed to one side, the fairness and rights of the other side are likely to reduce. In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers). From the perspective of providers, we consider the fairness of the providers' exposure in recommender system. For customers, we consider the fairness of the reduced quality of recommendation results due to the introduction of fairness measures. We theoretically analyzed the relationship between recommendation quality, customers fairness, and provider fairness, and design a two-sided fairness-aware recommendation model (TFROM) for both customers and providers. Specifically, we design two versions of TFROM for offline and online recommendation. The effectiveness of the model is verified on three real-world data sets. The experimental results show that TFROM provides better two-sided fairness while still maintaining a higher level of personalization than the baseline algorithms.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

22 of 40 references.

References · 22

  1. 2018A Reductions Approach to Fair Classificationcited by 585
  2. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  3. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  4. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  5. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  6. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  7. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  8. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  9. 2021Towards Long-term Fairness in Recommendationcited by 77
  10. 2017Multisided Fairness for Recommendationcited by 69
  11. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 67
  12. 2020FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learningcited by 44
10 more
  1. 2017Designing Fair Ranking Schemescited by 40
  2. 2018Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systemscited by 37
  3. 2020Correcting for Selection Bias in Learning-to-rank Systemscited by 35
  4. 2018Reducing Disparate Exposure in Ranking: A Learning To Rank Approachcited by 33
  5. 2020Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendationcited by 12
  6. 2020DeepFair: Deep Learning for Improving Fairness in Recommender Systemscited by 12
  7. 2018Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendationscited by 11
  8. 2020Opportunistic Multi-aspect Fairness through Personalized Re-rankingcited by 11
  9. 2018Personalizing Fairness-aware Re-rankingcited by 9
  10. 2020FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraintcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 10; 2023: 6; 2024: 7; 2025: 3; 2026: 3.

31 papers.

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  1. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  2. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  3. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  4. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  5. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  6. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  7. 2023Ensuring User-side Fairness in Dynamic Recommender Systemscited by 27
  8. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
  9. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  10. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  11. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  12. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
19 more
  1. 2024Mix of Experts Language Model for Named Entity Recognitioncited by 11
  2. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  3. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  4. 2024Intersectional Two-sided Fairness in Recommendationcited by 8
  5. 2022Fast online ranking with fairness of exposurecited by 5
  6. 2025Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender Systemcited by 5
  7. 2024Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generationcited by 4
  8. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
  9. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  10. 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
  11. 2022Who Pays? Personalization, Bossiness and the Cost of Fairnesscited by 2
  12. 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
  13. 2024Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approachcited by 2
  14. 2025Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimizationcited by 2
  15. 2024Content-Agnostic Moderation for Stance-Neutral Recommendationcited by 1
  16. 2026Measuring Individual User Fairness with User Similarity and Effectiveness Disparitycited by 1
  17. 2026Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendationcited by 1
  18. 2023FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resourcescited by 0
  19. 2026Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendationcited by 0