POI Recommendation of Location-Based Social Networks Using Tensor Factorization

With the rapid development of wireless communication technologies, location-based social networks (LBSNs) like foursquare and Gowalla have become very popular. Point of interest (POI) recommendation is a kind of important recommendation in LBSNs for enhancing user experiences. Unlike online social networks, LBSNs have a great deal of check-in data and comment information, which can provide valuable information for POI recommendation. In this paper, a novel recommendation strategy using tensor factorization is proposed for improving accurate rate of POI recommendation. Firstly, the latent dirichlet allocation(LDA) topic model is used to extract topic information and generate topic probability distribution of each POI based on comment information from users. Secondly, the check-in data of each user is divided into multiple data slices corresponding to each hour of a day. By connecting with the topic distributions of the visited POIs of each user, a user-topic-time tensor is conducted to present the potential preferences of all users. Finally, a higher order singular value decomposition (HOSVD) algorithm is employed to decompose the third-order tensor, to get dense preference information for POI recommendation. The experiments on a real dataset show that the proposed approach have better performance than the baseline methods.