Learn to Recommend Local Event Using Heterogeneous Social Networks

摘要：

　　Event-based social networks (EBSNs),which link the online and offline social networks,are increasing popular online services.Along with dramatic rise of the users and events in EBSNs,it is necessary to recommend event to users.Taking full advantage of social networks information can significantly improve predictive accuracy in recommender systems.The intuition here is that the users response to events are determined by his/her instinct and behaviours of friends.We propose a Heterogeneous Social Poisson Factorization(HSPF) model which combines online and offline social networks into one framework,and integrates the tie strength of online and offline friend relationships to the model.We test HSPF on Meetup dataset.Experimental results demonstrate that HSPF outperforms state-of-the-art recommendation methods.

关键词： Event recommendations Social recommendation Poisson factorization Event-based social networks

作者: Shaoqing Wang Zheng Wang Cuiping Li Kankan Zhao Hong Chen

作者单位: Key Lab of Data Engineering and Knowledge Engineering of MOE,Renmin University of China,Beijing,Chin Key Lab of Data Engineering and Knowledge Engineering of MOE,Renmin University of China,Beijing,Chin

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 169-182

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）