How does Fairness Matter in Group Recommendation

Abstract

Group recommendation has attracted significant research efforts for its importance in benefiting a group of users. In contrast to personalized recommendation, group recommendation tries to recommend same set of items to a group of users. Therefore a gap exists between the group recommendation and individual recommendation in terms of individual satisfaction. We aim to explore the possibility of narrowing this gap by introducing the concept of fairness in group recommendation.

In this work, we propose the concept of fairness in group recommendation and try to accommodate it into the recommendation algorithm so that the satisfaction of users in group recommendation can get close to that of individual recommendation. We utilize the concept of Ordered Weighted Average from fuzzy logic to evaluate the individual satisfaction of users and use min-max fairness metrics to accommodate the fairness into group recommendation process. We formulate the problem of group recommendation with fairness as an integer programming problem and propose efficient algorithms for three different OWA scenarios. Extensive experiments have been conducted on the real-world datasets and the results corroborate our analyses.