P-SERS: Personalized Social Event Recommender System for Online Group Buying Communities

• Main Authors: Yun-Hui Hung, 洪韻蕙
• Other Authors: Ming-Syan Chen
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/30924929342530591696

碩士 === 臺灣大學 === 電機工程學研究所 === 98 === 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. Therefore, social event recommendation draws lots of attention in recent days. In this work, we propose P-SERS, a Personalized Social Event Recommender System, which consists of three phases: (1) candidate selection, (2) social measurement and (3) recommendation. 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, (2) the participants and (3) the target item. These elements possess different types of in uential 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 in uence of participants and global popularity of the target item respectively. P-SERS evaluates each candidate social event by the social measures and produces a recommendation list. In addition, explanations and the grouping function are provided to improve the recommendation. 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.