A Hybrid Method for POI Recommendation:Combining Check-In Count, Geographical Information and Reviews

摘要：

　　Due to the rapid development of mobile devices,global position systems (GPS),Web 2.0 and location-based social networks (LBSNs) have attracted millions of users to share their locations or experiences.Point of Interest (POI) recommendation plays an important role in exploring attractive locations.POI recommendation is associated with multi-dimensional factors,such as check-in counts,geographical influence,and review text.Although GeoMF can model geographical influence well by matrix factorization (MF),it ignores the impact of review text for POI recommendation.We propose a hybrid method to joint check-in counts,geographical information and reviews for POI recommendation.Specifically,we connect check-in counts and geographical information by incorporating geographical information into matrix factorization.In addition,we combine check-in counts with review text by aligning latent check-in counts in MF,and utilize hidden review topics obtained from LDA by a transformation.The results of our experiments on the real-world dataset show that our proposed model can improve the performance of recommendation.

关键词： POI recommendation Geographical information Reviews

作者: Xiefeng Xu Pengpeng Zhao Guanfeng Liu Caidong Gu Jiajie Xu Jian Wu Zhiming Cui

作者单位: Department of Computer Science and Technology,Soochow University,Suzhou,China School of Computer Engineering,Suzhou Vocational University,Suzhou,China

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: 162-173

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）