# FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

arXiv: https://arxiv.org/abs/2005.01148 · HTML: https://mlsanity.com/paper/2005.01148/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 15 of 15)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2107.03415/) (2021)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation](https://mlsanity.com/paper/2501.07849/) (2025)
- [Exposing Product Bias in LLM Investment Recommendation](https://mlsanity.com/paper/2503.08750/) (2025)
- [Domain Incremental Lifelong Learning in an Open World](https://mlsanity.com/paper/2305.06555/) (2023)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation](https://mlsanity.com/paper/2211.01328/) (2022)
- [UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems](https://mlsanity.com/paper/2401.09034/) (2024)
- [Continuous Risk Prediction](https://mlsanity.com/paper/2410.09449/) (2024)
- [Fair Augmentation for Graph Collaborative Filtering](https://mlsanity.com/paper/2408.12208/) (2024)
- [Continuous QA Learning with Structured Prompts](https://mlsanity.com/paper/2208.14602/) (2022)
- [FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources](https://mlsanity.com/paper/2311.04542/) (2023)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

Recommender systems are often biased toward popular items. In other words, few items are frequently recommended while the majority of items do not get proportionate attention. That leads to low coverage of items in recommendation lists across users (i.e. low aggregate diversity) and unfair distribution of recommended items. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation for improving aggregate diversity. The algorithm iteratively finds items that are rarely recommended yet are high-quality and add them to the users' final recommendation lists. This is done by solving the maximum flow problem on the recommendation bipartite graph. While we focus on aggregate diversity and fair distribution of recommended items, the algorithm can be adapted to other recommendation scenarios using different underlying definitions of fairness. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improving aggregate diversity, provides comparable recommendation accuracy.