BIGFR: Bridging Individual and Group Fairness in Recommendation Systems

Abstract

Recommendation systems enhance user experience and retention by offering personalized content. As they increasingly influence social resource allocation (e.g., job recommendations), ensuring fair recommendations is becoming essential. Fairness notions in recommendation systems are mainly divided into individual and group fairness, and existing researchers notice the necessity of achieving both of them. However, the research indicates that there exists a trade-off relationship between them due to the way of defining individual fairness. Hence, existing methods attempt to redefine individual fairness. Unfortunately, they overlook the similarity comparison, which is essential for individual fairness. To address this issue, we redefine individual fairness from a ranking perspective. Based on this, we propose a framework that enhances both individual and group fairness. This framework includes a group fairness module, an individual fairness module, and a utility module. Extensive experiments show that our framework achieves a good balance between group fairness, individual fairness, and utility.