Data to 14 Oct 2026

A Survey on the Fairness of Recommender Systems

arXiv 2206.03761 ↗First posted 8 Jun 2022cs.IRcited by 102

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Abstract (arXiv)

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.

Key facts. Cited by 102 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2022.

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Citing papers per year: 2022: 6; 2023: 25; 2024: 32; 2025: 18; 2026: 21.

102 papers.

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2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
2023A Survey on Fairness in Large Language Modelscited by 95
2023A Survey on Popularity Bias in Recommender Systemscited by 43
2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
2024The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labelscited by 38
2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Surveycited by 30
2023Ensuring User-side Fairness in Dynamic Recommender Systemscited by 27
2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 27
2022Trustworthy Recommender Systemscited by 21

38 more

2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
2024Prospect Personalized Recommendation on Large Language Model-based Agent Platformcited by 19
2023No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendationcited by 18
2024SPRec: Self-Play to Debias LLM-based Recommendationcited by 18
2023Data Scarcity in Recommendation Systems: A Surveycited by 17
2023A Survey on Fairness-aware Recommender Systemscited by 14
2024Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patientscited by 12
2024A Survey of Controllable Learning: Methods and Applications in Information Retrievalcited by 12
2023Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentationcited by 11
2024Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendationcited by 10
2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10
2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
2024Popularity-Aware Alignment and Contrast for Mitigating Popularity Biascited by 9
2024Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMscited by 9
2023Learning with Exposure Constraints in Recommendation Systemscited by 8
2024Intersectional Two-sided Fairness in Recommendationcited by 8
2024SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agentcited by 8
2023Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learningcited by 6
2023Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Healthcited by 6
2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influencecited by 6
2026Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challengescited by 6
2024LinkSAGE: Optimizing Job Matching Using Graph Neural Networkscited by 5
2024Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Methodcited by 5
2022Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioningcited by 4
2025Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendationcited by 4
2023Uncovering Bias in Personal Informaticscited by 3
2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
2023Metrics for popularity bias in dynamic recommender systemscited by 3
2024Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimizationcited by 3
2024Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Trainingcited by 3
2025Diffusion Models in Recommendation Systems: A Surveycited by 3
2025Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimizationcited by 3
2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
2025Stairway to Fairness: Connecting Group and Individual Fairnesscited by 3
2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
2022The URW-KG: a Resource for Tackling the Underrepresentation of non-Western Writerscited by 2
2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
2024UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systemscited by 2

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