# A Survey on Fairness-aware Recommender Systems

arXiv: https://arxiv.org/abs/2306.00403 · HTML: https://mlsanity.com/paper/2306.00403/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 88 of its 181 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 30 of 88)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://mlsanity.com/paper/2010.15363/) (2020)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [Measuring Fairness in Ranked Outputs](https://mlsanity.com/paper/1610.08559/) (2016)
- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://mlsanity.com/paper/1909.03601/) (2019)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Towards Long-term Fairness in Recommendation](https://mlsanity.com/paper/2101.03584/) (2021)
- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [Fairness-Aware Explainable Recommendation over Knowledge Graphs](https://mlsanity.com/paper/2006.02046/) (2020)
- [The Unfairness of Popularity Bias in Recommendation](https://mlsanity.com/paper/1907.13286/) (2019)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Deconfounded Recommendation for Alleviating Bias Amplification](https://mlsanity.com/paper/2105.10648/) (2021)
- [Learning Fair Representations for Recommendation: A Graph-based Perspective](https://mlsanity.com/paper/2102.09140/) (2021)
- [FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning](https://mlsanity.com/paper/2006.16742/) (2020)
- [A Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2207.12515/) (2022)
- [Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems](https://mlsanity.com/paper/2005.12964/) (2020)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)

## Built on by (most cited first, 14 of 14)

- [A Survey on Fairness in Large Language Models](https://mlsanity.com/paper/2308.10149/) (2023)
- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation](https://mlsanity.com/paper/2405.14407/) (2024)
- [GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks](https://mlsanity.com/paper/2312.07861/) (2023)
- [Intersectional Unfairness Discovery](https://mlsanity.com/paper/2405.20790/) (2024)
- [Ethical Framework for Responsible Foundational Models in Medical Imaging](https://mlsanity.com/paper/2406.11868/) (2024)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation](https://mlsanity.com/paper/2411.19678/) (2024)
- [Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study](https://mlsanity.com/paper/2502.20354/) (2025)
- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)
- [Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach](https://mlsanity.com/paper/2510.21728/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.