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Exploring temporal effects for location recommendation on location-based social networks.
| Content Provider | CiteSeerX |
|---|---|
| Author | Gao, Huiji Tang, Jiliang Hu, Xia Liu, Huan |
| 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Location Recommendation Location-based Social Network Temporal Effect Human Behavior Location Recommendation Performance Temporal Property Location-based Service Previous Work User Movement Strong Correlation Temporal Pattern User Behavior Inordinate Number Novel Location Recommendation Framework Urban Experience Recent Year Recommender System User Check-in Time Experimental Result Social Information Real-world Lbsn Dataset Various Aspect Social Influence Corresponding Check-in Location Online Lbsns Unprecedented Opportunity |
| Content Type | Text |
