Personalizing Fairness-aware Re-ranking

TL;DR

This paper introduces a personalized fairness-aware re-ranking algorithm that balances recommendation accuracy with provider fairness, considering individual user tolerance for diversity, demonstrated through experiments on synthetic and real data.

Contribution

The paper proposes a novel re-ranking algorithm that personalizes fairness considerations in recommendations, balancing provider fairness and user diversity preferences.

Findings

Significantly improves provider fairness in recommendations.

Maintains high recommendation accuracy with slight trade-offs.

Effectively incorporates user-specific diversity tolerance.

Abstract

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.