An Adaptive POI Recommendation Algorithm by Integrating User's Temporal and Spatial Features in LBSNs

Abstract

With the rapid development of Internet technology and global positioning technology, location-based social networks (LBSNs) have emerged. Massive check-in data generated by LBSNs can be employed to explore users' behavior preferences, and enable a wide range of location-based social services. The most straightforward service is Point of Interest (POI) recommendation, which help users to discover their desired places. In order to achieve effective and precise POI recommendation, complicated high-dimensional data need to be handled, i.e., user-geographical position-visiting time-category of the visiting place. Besides, such data usually suffer from a data sparsity problem. To this end, this paper proposes a novel POI recommendation model that incorporates spatial and temporal preferences of the users. We propose a tensor factorization-based approach and a Voronoi diagram-based approach to model the impact of temporal and spatial features on users' preference, respectively. Then, a fusion framework is proposed to calculate similarities between different uses by combining both temporal and spatial preferences of the users. Finally, an adaptive collaborative filtering approach is applied to generate recommendation list. The experiments results on the Foursquare and the Gowalla datasets shows that our model has higher recommendation performance than other comparison models.