# Personalizing Fairness-aware Re-ranking

arXiv: https://arxiv.org/abs/1809.02921 · HTML: https://mlsanity.com/paper/1809.02921/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Fairness-Aware Graph Neural Networks: A Survey](https://mlsanity.com/paper/2307.03929/) (2023)
- [Dynamic fairness-aware recommendation through multi-agent social choice](https://mlsanity.com/paper/2303.00968/) (2023)
- [Matrix Estimation for Individual Fairness](https://mlsanity.com/paper/2302.02096/) (2023)
- [Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF](https://mlsanity.com/paper/2309.08621/) (2023)
- [Countering Mainstream Bias via End-to-End Adaptive Local Learning](https://mlsanity.com/paper/2404.08887/) (2024)
- [Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study](https://mlsanity.com/paper/2607.27577/) (2026)

## Abstract (arXiv)

Personalized recommendation brings about novel challenges in ensuring fairness, especially in scenarios in which users are not the only stakeholders involved in the recommender system. For example, the system may want to ensure that items from different providers have a fair chance of being recommended. To solve this problem, we propose a Fairness-Aware Re-ranking algorithm (FAR) to balance the ranking quality and provider-side fairness. We iteratively generate the ranking list by trading off between accuracy and the coverage of the providers. Although fair treatment of providers is desirable, users may differ in their receptivity to the addition of this type of diversity. Therefore, personalized user tolerance towards provider diversification is incorporated. Experiments are conducted on both synthetic and real-world data. The results show that our proposed re-ranking algorithm can significantly promote fairness with a slight sacrifice in accuracy and can do so while being attentive to individual user differences.