# Measuring Individual User Fairness with User Similarity and Effectiveness Disparity

arXiv: https://arxiv.org/abs/2602.02516 · HTML: https://mlsanity.com/paper/2602.02516/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 23 of 23)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://mlsanity.com/paper/2202.06200/) (2022)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Fairness-Aware Explainable Recommendation over Knowledge Graphs](https://mlsanity.com/paper/2006.02046/) (2020)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems](https://mlsanity.com/paper/1812.01504/) (2018)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Online certification of preference-based fairness for personalized recommender systems](https://mlsanity.com/paper/2104.14527/) (2021)
- [Fairness and Diversity in Recommender Systems: A Survey](https://mlsanity.com/paper/2307.04644/) (2023)
- [User Fairness in Recommender Systems](https://mlsanity.com/paper/1807.06349/) (2018)
- [Inherent Limitations of AI Fairness](https://mlsanity.com/paper/2212.06495/) (2022)
- [Towards Fair Recommendation in Two-Sided Platforms](https://mlsanity.com/paper/2201.01180/) (2021)
- [Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics](https://mlsanity.com/paper/2202.01615/) (2022)
- [Evaluation Measures for Relevance and Credibility in Ranked Lists](https://mlsanity.com/paper/1708.07157/) (2017)
- [Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study](https://mlsanity.com/paper/2311.01013/) (2023)
- [Principled Multi-Aspect Evaluation Measures of Rankings](https://mlsanity.com/paper/2212.00492/) (2022)
- [Stairway to Fairness: Connecting Group and Individual Fairness](https://mlsanity.com/paper/2508.21334/) (2025)

## Built on by (most cited first, 1 of 1)

- [Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders](https://mlsanity.com/paper/2608.08284/) (2026)

## Abstract (arXiv)

Individual user fairness is commonly understood as treating similar users similarly. In Recommender Systems (RSs), several evaluation measures exist for quantifying individual user fairness. These measures evaluate fairness via either: (i) the disparity in RS effectiveness scores regardless of user similarity, or (ii) the disparity in items recommended to similar users regardless of item relevance. Both disparity in recommendation effectiveness and user similarity are very important in fairness, yet no existing individual user fairness measure simultaneously accounts for both. In brief, current user fairness evaluation measures implement a largely incomplete definition of fairness. To fill this gap, we present Pairwise User unFairness (PUF), a novel evaluation measure of individual user fairness that considers both effectiveness disparity and user similarity. PUF is the only measure that can express this important distinction. We empirically validate that PUF does this consistently across 4 datasets and 7 rankers, and robustly when varying user similarity or effectiveness. In contrast, all other measures are either almost insensitive to effectiveness disparity or completely insensitive to user similarity. We contribute the first RS evaluation measure to reliably capture both user similarity and effectiveness in individual user fairness. Our code: https://github.com/theresiavr/PUF-individual-user-fairness-recsys.