FAGR: Fairness-aware Group Recommendation in Event-based Social Networks

Abstract

An event-based social network (EBSN) is a new type of social network that combines online and offline n etworks. In recent years, an important task in EBSN recommendation systems has been to design better and more reasonable recommendation algorithms to improve the accuracy of recommendation and enhance user satisfaction. However, the current research seldom considers how to coordinate fairness among individual users and reduce the impact of individual unreasonable feedback in group event recommendation. In addition, while considering the fairness of individuals, the accuracy of recommendation is less improved by fully combining the context key information. To solve these problems, we propose a prefiltering algorithm to filter t he c andidate e vent s et, a m ultidimensional context recommendation to provide personalized event recommendations for each user in the group, and a group consensus function fusion strategy to fuse the recommendation results of members in the group. To improve overall satisfaction with the recommendations, we propose a ranking adjustment strategy of the key context. Finally, we verify the effectiveness of our proposed algorithm in real data sets and find t hat F AGR i s s uperior t o t he latest algorithms in terms of global satisfaction, distance satisfaction and user fairness.