A sentiment-enhanced personalized location recommendation system

Dingqi Yang, Daqing Zhang, Zhiyong Yu, Zhu Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

209 引用 (Scopus)

摘要

Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe users' preference of venues. However, in current research work, only users' check-in behavior is considered in users' location preference model, users' tips on venues are seldom investigated yet. Moreover, while existing work mainly considers social influence in recommendation, we argue that considering venue similarity can further improve the recommendation performance. In this research, we ameliorate location recommendation by enhancing not only the user location preference model but also recommendation algorithm. First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques. Second, we develop a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation. Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining the preference consistency, and the proposed algorithm outperforms the state-of-the-art methods.

源语言英语
主期刊名HT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media
119-128
页数10
DOI
出版状态已出版 - 2013
活动24th ACM Conference on Hypertext and Social Media, HT 2013 - Paris, 法国
期限: 1 5月 20133 5月 2013

出版系列

姓名HT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media

会议

会议24th ACM Conference on Hypertext and Social Media, HT 2013
国家/地区法国
Paris
时期1/05/133/05/13

指纹

探究 'A sentiment-enhanced personalized location recommendation system' 的科研主题。它们共同构成独一无二的指纹。

引用此