What your images reveal: Exploiting visual contents for point-of-interest recommendation

Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, Huan Liu

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

104 Scopus citations

Abstract

The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographical influence, social correlations and textual content indications. For example, user’s visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload photos associating with locations. Photos not only reflect users’ interests but also provide informative descriptions about locations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recommendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommendation (VPOI), which incorporates visual contents for POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference, WWW 2017
PublisherInternational World Wide Web Conferences Steering Committee
Pages391-400
Number of pages10
ISBN (Print)9781450349130
DOIs
StatePublished - Jan 1 2017
Event26th International World Wide Web Conference, WWW 2017 - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Publication series

Name26th International World Wide Web Conference, WWW 2017

Conference

Conference26th International World Wide Web Conference, WWW 2017
CountryAustralia
CityPerth
Period4/3/174/7/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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