# Multi-sided Exposure Bias in Recommendation

arXiv: https://arxiv.org/abs/2006.15772 · HTML: https://mlsanity.com/paper/2006.15772/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 23 of 23)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [AutoDebias: Learning to Debias for Recommendation](https://mlsanity.com/paper/2105.04170/) (2021)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios](https://mlsanity.com/paper/2110.06475/) (2021)
- [A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2107.03415/) (2021)
- [Deconfounded Causal Collaborative Filtering](https://mlsanity.com/paper/2110.07122/) (2021)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [Cross Pairwise Ranking for Unbiased Item Recommendation](https://mlsanity.com/paper/2204.12176/) (2022)
- [STAR: A Simple Training-free Approach for Recommendations using Large Language Models](https://mlsanity.com/paper/2410.16458/) (2024)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://mlsanity.com/paper/2411.06624/) (2024)
- [Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation](https://mlsanity.com/paper/2301.07483/) (2023)
- [CDR: Conservative Doubly Robust Learning for Debiased Recommendation](https://mlsanity.com/paper/2308.08461/) (2023)
- [Exploring and Exploiting Data Heterogeneity in Recommendation](https://mlsanity.com/paper/2305.15431/) (2023)
- [Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation](https://mlsanity.com/paper/2308.08799/) (2023)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics](https://mlsanity.com/paper/2305.18333/) (2023)
- [Label Denoising through Cross-Model Agreement](https://mlsanity.com/paper/2308.13976/) (2023)
- [Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations](https://mlsanity.com/paper/2310.03148/) (2023)
- [BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model](https://mlsanity.com/paper/2512.13848/) (2025)

## Abstract (arXiv)

Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, diversity, and novelty are studied, it is often solely from the users' perspective. However, many real-world recommenders are often multi-stakeholder environments in which the needs and interests of several stakeholders should be addressed in the recommendation process. In this paper, we focus on the popularity bias problem which is a well-known property of many recommendation algorithms where few popular items are over-recommended while the majority of other items do not get proportional attention and address its impact on different stakeholders. Using several recommendation algorithms and two publicly available datasets in music and movie domains, we empirically show the inherent popularity bias of the algorithms and how this bias impacts different stakeholders such as users and suppliers of the items. We also propose metrics to measure the exposure bias of recommendation algorithms from the perspective of different stakeholders.