# Crank up the volume: preference bias amplification in collaborative recommendation

arXiv: https://arxiv.org/abs/1909.06362 · HTML: https://mlsanity.com/paper/1909.06362/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Debiasing Learning for Membership Inference Attacks Against Recommender Systems](https://mlsanity.com/paper/2206.12401/) (2022)
- [Unbiased Knowledge Distillation for Recommendation](https://mlsanity.com/paper/2211.14729/) (2022)
- [Unintended Bias in Language Model-driven Conversational Recommendation](https://mlsanity.com/paper/2201.06224/) (2022)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation](https://mlsanity.com/paper/2109.05931/) (2021)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [Datasets for Navigating Sensitive Topics in Recommendation Systems](https://mlsanity.com/paper/2509.07269/) (2025)
- [DPA: A one-stop metric to measure bias amplification in classification datasets](https://mlsanity.com/paper/2412.11060/) (2024)
- [A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions](https://mlsanity.com/paper/2503.07878/) (2025)
- [Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations](https://mlsanity.com/paper/2310.03148/) (2023)
- [Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems](https://mlsanity.com/paper/2506.18327/) (2025)

## Abstract (arXiv)

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine bias disparity over a range of different algorithms and for different item categories and demonstrate significant differences between model-based and memory-based algorithms.