TY - GEN
T1 - A sentiment-enhanced personalized location recommendation system
AU - Yang, Dingqi
AU - Zhang, Daqing
AU - Yu, Zhiyong
AU - Wang, Zhu
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Location based social networks
KW - Matrix factorization
KW - Recommendation system
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=84879771096&partnerID=8YFLogxK
U2 - 10.1145/2481492.2481505
DO - 10.1145/2481492.2481505
M3 - 会议稿件
AN - SCOPUS:84879771096
SN - 9781450319676
T3 - HT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media
SP - 119
EP - 128
BT - HT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media
T2 - 24th ACM Conference on Hypertext and Social Media, HT 2013
Y2 - 1 May 2013 through 3 May 2013
ER -