# Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

arXiv: https://arxiv.org/abs/1809.03577 · HTML: https://mlsanity.com/paper/1809.03577/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://mlsanity.com/paper/2211.05105/) (2022)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [The Impossibility of Fair LLMs](https://mlsanity.com/paper/2406.03198/) (2024)
- [What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models](https://mlsanity.com/paper/2102.03692/) (2021)
- [Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images](https://mlsanity.com/paper/2401.14322/) (2024)
- [Subverting Fair Image Search with Generative Adversarial Perturbations](https://mlsanity.com/paper/2205.02414/) (2022)
- [Fairness Explainability using Optimal Transport with Applications in Image Classification](https://mlsanity.com/paper/2308.11090/) (2023)
- [Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](https://mlsanity.com/paper/2406.14004/) (2024)
- [EquiPy: Sequential Fairness using Optimal Transport in Python](https://mlsanity.com/paper/2503.09866/) (2025)
- [Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems](https://mlsanity.com/paper/2506.18327/) (2025)

## Abstract (arXiv)

The trade-off between relevance and fairness in personalized recommendations has been explored in recent works, with the goal of minimizing learned discrimination towards certain demographics while still producing relevant results. We present a fairness-aware variation of the Maximal Marginal Relevance (MMR) re-ranking method which uses representations of demographic groups computed using a labeled dataset. This method is intended to incorporate fairness with respect to these demographic groups. We perform an experiment on a stock photo dataset and examine the trade-off between relevance and fairness against a well known baseline, MMR, by using human judgment to examine the results of the re-ranking when using different fractions of a labeled dataset, and by performing a quantitative analysis on the ranked results of a set of query images. We show that our proposed method can incorporate fairness in the ranked results while obtaining higher precision than the baseline, while our case study shows that even a limited amount of labeled data can be used to compute the representations to obtain fairness. This method can be used as a post-processing step for recommender systems and search.