TICRec : A probabilistic framework to utilize temporal influence correlations for time-aware location recommendations

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

46 Scopus Citations
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Detail(s)

Original languageEnglish
Article number7061519
Pages (from-to)633-646
Journal / PublicationIEEE Transactions on Services Computing
Volume9
Issue number4
Online published16 Mar 2015
Publication statusPublished - Jul 2016

Abstract

In location-based social networks (LBSNs), time significantly affects users' check-in behaviors, for example, people usually visit different places at different times of weekdays and weekends, e.g., restaurants at noon on weekdays and bars at midnight on weekends. Current studies use the temporal influence to recommend locations through dividing users' check-in locations into time slots based on their check-in time and learning their preferences to locations in each time slot separately. Unfortunately, these studies generally suffer from two major limitations: (1) the loss of time information because of dividing a day into time slots and (2) the lack of temporal influence correlations due to modeling users' preferences to locations for each time slot separately. In this paper, we propose a probabilistic framework called TICRec that utilizes temporal influence correlations (TIC) of both weekdays and weekends for time-aware location recommendations. TICRec not only recommends locations to users, but it also suggests when a user should visit a recommended location. In TICRec, we estimate a time probability density of a user visiting a new location without splitting the continuous time into discrete time slots to avoid the time information loss. To leverage the TIC, TICRec considers both user-based TIC (i.e., different users' check-in behaviors to the same location at different times) and location-based TIC (i.e., the same user's check-in behaviors to different locations at different times). Finally, we conduct a comprehensive performance evaluation for TICRec using two real data sets collected from Foursquare and Gowalla. Experimental results show that TICRec achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques with temporal influence.

Research Area(s)

  • Continuous temporal influence, Kernel density estimation, Location recommendations, Location-based social networks, Temporal influence correlations, Time-aware location recommendations