# Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

arXiv: https://arxiv.org/abs/1908.00831 · HTML: https://mlsanity.com/paper/1908.00831/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation](https://mlsanity.com/paper/2504.07911/) (2025)
- [Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems](https://mlsanity.com/paper/2506.18327/) (2025)

## Abstract (arXiv)

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.