A point-of-interest suggestion algorithm in Multi-source geo-social networks

Newly emerging location-based social network (LBSN) services provide us with new platforms to share interests and individual experience based on their activity history. The problems of data sparsity and user distrust in LBSNs create a severe challenge for traditional recommender systems. Moreover, users' behaviors in LBSNs show an obvious spatio-temporal pattern. Valuable extra information from microblog-based social networks (MBSNs) can be utilized to improve the effectiveness of POI suggestion. In this study, we propose a latent probabilistic generative model called MTAS, which can accurately capture the underlying information in users' words extracted from both LBSNs and MBSNs by taking into consideration the decision probability, a latent variable indicating a user's tendency to publish a review in LBSNs or MBSNs. Then, the parameters of the MTAS model can be inferred by the Gibbs sampling method in an effective manner. Based on MTAS, we design an effective framework to fulfill the top-k suggestion. Extensive experiments on two real geo-social networks show that MTAS achieves better performance than existing state-of-the-art methods.