TY - GEN
T1 - Location recommendation for out-of-town users in location-based social networks
AU - Ference, Gregory
AU - Ye, Mao
AU - Lee, Wang-chien
PY - 2013
Y1 - 2013
N2 - Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for out-of-town users by taking into account user preference, social influence and geographical proximity. Accordingly, we propose a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering (UPS-CF), to make location recommendation for mobile users in LBSNs. We validate our ideas by comprehensive experiments using real datasets collected from Foursquare and Gowalla. By comparing baseline algorithms and conventional collaborative filtering approach (and its variants), we show that UPS-CF exhibits the best performance. Additionally, we find that preference derived from similar users is important for in-town users while social influence becomes more important for out-of-town users. Copyright is held by the owner/author(s).
AB - Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for out-of-town users by taking into account user preference, social influence and geographical proximity. Accordingly, we propose a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering (UPS-CF), to make location recommendation for mobile users in LBSNs. We validate our ideas by comprehensive experiments using real datasets collected from Foursquare and Gowalla. By comparing baseline algorithms and conventional collaborative filtering approach (and its variants), we show that UPS-CF exhibits the best performance. Additionally, we find that preference derived from similar users is important for in-town users while social influence becomes more important for out-of-town users. Copyright is held by the owner/author(s).
UR - http://www.scopus.com/inward/record.url?scp=84889606960&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889606960&partnerID=8YFLogxK
U2 - 10.1145/2505515.2505637
DO - 10.1145/2505515.2505637
M3 - Conference contribution
AN - SCOPUS:84889606960
SN - 9781450322638
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 721
EP - 726
BT - CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
T2 - 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Y2 - 27 October 2013 through 1 November 2013
ER -