# Fairness in Recommendation: Foundations, Methods and Applications

arXiv: https://arxiv.org/abs/2205.13619 · HTML: https://mlsanity.com/paper/2205.13619/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 153 of its 283 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 30 of 153)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [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)
- [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Controllable Invariance through Adversarial Feature Learning](https://mlsanity.com/paper/1705.11122/) (2017)

## Built on by (most cited first, 27 of 27)

- [Fairness of ChatGPT](https://mlsanity.com/paper/2305.18569/) (2023)
- [SPRec: Self-Play to Debias LLM-based Recommendation](https://mlsanity.com/paper/2412.09243/) (2024)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems](https://mlsanity.com/paper/2401.04057/) (2024)
- [Recommender Systems for Sustainability: Overview and Research Issues](https://mlsanity.com/paper/2412.03620/) (2024)
- [Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges](https://mlsanity.com/paper/2406.06736/) (2024)
- [Stairway to Fairness: Connecting Group and Individual Fairness](https://mlsanity.com/paper/2508.21334/) (2025)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach](https://mlsanity.com/paper/2401.02552/) (2024)
- [Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations](https://mlsanity.com/paper/2601.20449/) (2026)
- [Exploring and Exploiting Data Heterogeneity in Recommendation](https://mlsanity.com/paper/2305.15431/) (2023)
- [Bipartite Graph Variational Auto-Encoder with Fair Latent Representation to Account for Sampling Bias in Ecological Networks](https://mlsanity.com/paper/2403.02011/) (2024)
- [Recommending the right academic programs: An interest mining approach using BERTopic](https://mlsanity.com/paper/2501.06581/) (2025)
- [Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model](https://mlsanity.com/paper/2506.19777/) (2025)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Measuring Individual User Fairness with User Similarity and Effectiveness Disparity](https://mlsanity.com/paper/2602.02516/) (2026)
- [dsld: A Socially Relevant Tool for Teaching Statistics](https://mlsanity.com/paper/2411.04228/) (2024)
- [Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?](https://mlsanity.com/paper/2411.17374/) (2024)
- [Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study](https://mlsanity.com/paper/2502.20354/) (2025)
- [Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning](https://mlsanity.com/paper/2508.11943/) (2025)
- [Understanding Distribution Structure on Calibrated Recommendation Systems](https://mlsanity.com/paper/2508.13568/) (2025)
- [Model-agnostic post-hoc explainability for recommender systems](https://mlsanity.com/paper/2509.10245/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)
- [Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation](https://mlsanity.com/paper/2601.20848/) (2026)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)
- [Fairness Attacks on Recommender Systems](https://mlsanity.com/paper/2606.29064/) (2026)
- [Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders](https://mlsanity.com/paper/2607.10235/) (2026)

## Abstract (arXiv)

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.