Location recommendation for out-of-town users in location-based social networks

Authors:
Gregory Ference;Mao Ye;Wang-Chien Lee
Affiliations:
The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

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.