# New Fairness Metrics for Recommendation that Embrace Differences

arXiv: https://arxiv.org/abs/1706.09838 · HTML: https://mlsanity.com/paper/1706.09838/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images](https://mlsanity.com/paper/2311.14084/) (2023)
- [Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos](https://mlsanity.com/paper/2502.07327/) (2025)
- [Aggregating User Preferences while Ensuring Equity, Diversity, and Inclusion using Graph Summarization](https://mlsanity.com/paper/2610.07128/) (2026)

## Abstract (arXiv)

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.