| Summary: | 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.
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