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

Gregory Ference, Mao Ye, Wang-chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

96 Scopus citations

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. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages721-726
Number of pages6
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Ference, G., Ye, M., & Lee, W. (2013). Location recommendation for out-of-town users in location-based social networks. In CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (pp. 721-726). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2505515.2505637