P-SERS: Personalized social event recommender system

Abstract

As the increasing popularity of social networking functions, people interact with others in social events everyday. However, people are easily overwhelmed by hundreds of social events. In this work, we propose P-SERS, a Personalized Social Event Recommender System, which consists of three phases: (1) Candidate se-lection, (2) Social measurement and (3) Recommendation. Among these, potential candidate events are selected based on user preference and the social network. In our opinion, every social event is composed of three critical elements: (1) the initiator, s(2) the participants and (3) the target item. These elements possess different types of influential power on a social event. Therefore, we design algorithms to compute three social measures, i.e., initiator score, participant score and target score, which model expertise of the initiator, group influence of participants and global popularity of the target item respectively. P-SERS evaluates each candidate social event by these social measures and produces a recommendation list. In addition, explanations and the grouping function are provided to improve the recommenda-tion. Finally, we examine P-SERS by recommending group buying events in a real world online group buying website. The experimental results show the superiority of P-SERS over conventional social recommendation methods.