# User Fairness in Recommender Systems

arXiv: https://arxiv.org/abs/1807.06349 · HTML: https://mlsanity.com/paper/1807.06349/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 21 of 21)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Trustworthy Recommender Systems](https://mlsanity.com/paper/2208.06265/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Federated Fairness Analytics: Quantifying Fairness in Federated Learning](https://mlsanity.com/paper/2408.08214/) (2024)
- [Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?](https://mlsanity.com/paper/2101.10905/) (2021)
- [Uncovering Bias in Personal Informatics](https://mlsanity.com/paper/2303.15592/) (2023)
- [FairRec: Fairness Testing for Deep Recommender Systems](https://mlsanity.com/paper/2304.07030/) (2023)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [Stairway to Fairness: Connecting Group and Individual Fairness](https://mlsanity.com/paper/2508.21334/) (2025)
- [UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems](https://mlsanity.com/paper/2401.09034/) (2024)
- [Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2606.18111/) (2026)
- [Measuring Individual User Fairness with User Similarity and Effectiveness Disparity](https://mlsanity.com/paper/2602.02516/) (2026)
- [Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace](https://mlsanity.com/paper/2203.03718/) (2022)
- [Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems](https://mlsanity.com/paper/2505.19441/) (2025)
- [SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models](https://mlsanity.com/paper/2604.09553/) (2026)
- [Aggregating User Preferences while Ensuring Equity, Diversity, and Inclusion using Graph Summarization](https://mlsanity.com/paper/2610.07128/) (2026)

## Abstract (arXiv)

Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.