Huan.: Exploring temporal effects for location recommendation on location-based social networks

Location-based social networks (LBSNs) have attracted an inordi-nate number of users and greatly enriched the urban experience in recent years. The availability of spatial, temporal and social in-formation 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. Pre-vious work studied spatial and social influences on location rec-ommendation 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 in-evitably need to consider temporal effects. In this paper, we in-troduce 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