Exploring temporal effects for location recommendation on location-based social networks

Huiji Gao, Jiliang Tang, Xia Hu, Huan Liu

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

303 Scopus citations

Abstract

Location-based social networks (LBSNs) have attracted an inordinate number of users and greatly enriched the urban experience in recent years. The availability of spatial, temporal and social information in online LBSNs offers an unprecedented opportunity to study various aspects of human behavior, and enable a variety of location-based services such as location recommendation. Previous work studied spatial and social influences on location recommendation in LBSNs. Due to the strong correlations between a user's check-in time and the corresponding check-in location, recommender systems designed for location recommendation inevitably need to consider temporal effects. In this paper, we introduce a novel location recommendation framework, based on the temporal properties of user movement observed from a real-world LBSN dataset. The experimental results exhibit the significance of temporal patterns in explaining user behavior, and demonstrate their power to improve location recommendation performance.

Original languageEnglish (US)
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages93-100
Number of pages8
DOIs
StatePublished - Nov 20 2013
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
CountryChina
CityHong Kong
Period10/12/1310/16/13

Keywords

  • Location recommendation
  • Location-based social networks
  • Temporal effects

ASJC Scopus subject areas

  • Software

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  • Cite this

    Gao, H., Tang, J., Hu, X., & Liu, H. (2013). Exploring temporal effects for location recommendation on location-based social networks. In RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems (pp. 93-100). (RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems). https://doi.org/10.1145/2507157.2507182