mlsanityData to 7 Oct 2026

Fairness in Recommender Systems: Research Landscape and Future Directions

arXiv 2205.11127 ↗First posted 23 May 2022cs.IR cs.AIcited by 30

Not on the map. Why?

Abstract (arXiv)

Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 72 of its 193 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

72 of 193 references.

References · 72

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  3. 2017Counterfactual Fairnesscited by 802
  4. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  5. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  6. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  7. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  8. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
  9. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  10. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  11. 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
  12. 2019Managing Popularity Bias in Recommender Systems with Personalized Re-rankingcited by 81
38 more
  1. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  2. 2021Towards Long-term Fairness in Recommendationcited by 77
  3. 2021User-oriented Fairness in Recommendationcited by 75
  4. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  5. 2017Multisided Fairness for Recommendationcited by 69
  6. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  7. 2018RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertisingcited by 65
  8. 2021Fairness in Rankings and Recommendations: An Overviewcited by 62
  9. 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
  10. 2020A systematic review and taxonomy of explanations in decision support and recommender systemscited by 45
  11. 2020FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learningcited by 44
  12. 2021Characterizing Intersectional Group Fairness with Worst-Case Comparisonscited by 42
  13. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  14. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  15. 2018Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systemscited by 37
  16. 2020The Connection Between Popularity Bias, Calibration, and Fairness in Recommendationcited by 35
  17. 2019Measuring the Business Value of Recommender Systemscited by 32
  18. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  19. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  20. 2021Fairness in Information Access Systemscited by 27
  21. 2019Auditing and Achieving Intersectional Fairness in Classification Problemscited by 25
  22. 2022A Review of Modern Fashion Recommender Systemscited by 25
  23. 2019This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technologycited by 24
  24. 2019Addressing Marketing Bias in Product Recommendationscited by 23
  25. 2021Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendationcited by 23
  26. 2021When Fair Ranking Meets Uncertain Inferencecited by 22
  27. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  28. 2019Multi-stakeholder Recommendation and its Connection to Multi-sided Fairnesscited by 19
  29. 2018A Fairness-aware Hybrid Recommender Systemcited by 18
  30. 2019Policy-Gradient Training of Fair and Unbiased Ranking Functionscited by 16
  31. 2020Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systemscited by 15
  32. 2021Estimation of Fair Ranking Metrics with Incomplete Judgmentscited by 14
  33. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  34. 2018Bias Disparity in Recommendation Systemscited by 12
  35. 2019Crank up the volume: preference bias amplification in collaborative recommendationcited by 12
  36. 2020Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendationcited by 12
  37. 2020DeepFair: Deep Learning for Improving Fairness in Recommender Systemscited by 12
  38. 2020Exploring Artist Gender Bias in Music Recommendationcited by 12

50 most cited of 72 All 72 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 5; 2025: 10; 2026: 8.

30 papers.

Sort:

By area:

  1. 2023A Survey on Popularity Bias in Recommender Systemscited by 42
  2. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 27
  3. 2023Fairness of ChatGPT and the Role Of Explainable-Guided Promptscited by 13
  4. 2024Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendationcited by 10
  5. 2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
  6. 2026LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systemscited by 5
  7. 2024A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender Systemcited by 4
  8. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
  9. 2023Metrics for popularity bias in dynamic recommender systemscited by 3
  10. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  11. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  12. 2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
18 more
  1. 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
  2. 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
  3. 2023Fairness Vs. Personalization: Towards Equity in Epistemic Utilitycited by 2
  4. 2024Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matterscited by 2
  5. 2025Recommending the right academic programs: An interest mining approach using BERTopiccited by 1
  6. 2025A Reproducibility Study of Product-side Fairness in Bundle Recommendationcited by 1
  7. 2025Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluationcited by 1
  8. 2026Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanationscited by 1
  9. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1
  10. 2025Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Studycited by 0
  11. 2025PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systemscited by 0
  12. 2025Understanding Distribution Structure on Calibrated Recommendation Systemscited by 0
  13. 2025Model-agnostic post-hoc explainability for recommender systemscited by 0
  14. 2026A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender Systemcited by 0
  15. 2026Aligning Recommendations with User Popularity Preferencescited by 0
  16. 2026Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systemscited by 0
  17. 2026COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphscited by 0
  18. 2026Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenderscited by 0