# A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems

arXiv: https://arxiv.org/abs/2107.03415 · HTML: https://mlsanity.com/paper/2107.03415/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [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)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [Multi-sided Exposure Bias in Recommendation](https://mlsanity.com/paper/2006.15772/) (2020)
- [Deconvolving Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1703.01049/) (2017)
- [FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems](https://mlsanity.com/paper/2005.01148/) (2020)
- [Fair-by-design matching](https://mlsanity.com/paper/1802.02562/) (2018)
- [Modeling and Counteracting Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2001.04832/) (2020)
- [Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration](https://mlsanity.com/paper/2007.12230/) (2020)

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

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems](https://mlsanity.com/paper/2210.12546/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization](https://mlsanity.com/paper/2603.19649/) (2026)
- [Intersectional Two-sided Fairness in Recommendation](https://mlsanity.com/paper/2402.02816/) (2024)
- [How Fair is Your Diffusion Recommender Model?](https://mlsanity.com/paper/2409.04339/) (2024)
- [Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery](https://mlsanity.com/paper/2308.14601/) (2023)
- [Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations](https://mlsanity.com/paper/2405.17607/) (2024)
- [Exploring and Exploiting Data Heterogeneity in Recommendation](https://mlsanity.com/paper/2305.15431/) (2023)
- [A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems](https://mlsanity.com/paper/2301.07639/) (2023)
- [Systemic Fairness](https://mlsanity.com/paper/2304.06901/) (2023)
- [FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources](https://mlsanity.com/paper/2311.04542/) (2023)
- [Estimating Fair Graphs from Graph-Stationary Data](https://mlsanity.com/paper/2510.07536/) (2025)
- [Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It](https://mlsanity.com/paper/2610.03195/) (2026)

## Abstract (arXiv)

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end-user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increasing aggregate diversity in order to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high quality items that have low visibility or items from suppliers with low exposure to the users' final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.