distance  [@Zheng_www09; @Ye_gis10; @Cho_kdd11]. For example, Ye et al.  proposed a Bayesian collaborative filtering (CF) algorithm to explore the geographical influence. Cheng et al.   captured the geographical influence by modeling the probability of a user’s check-in on a location as a multi-center Gaussian model and then combined it into a generalized matrix factorization model. Lian et al.   adopted a weighted matrix factorization framework to incorporate the spatial clustering phenomenon.

Similar to the geo-spatial information, time is another important factor in POI recommendation. Ye et al.,   found the periodic temporal property that people usually go to restaurants at around noon and visit clubs at night. Yuan et al.,   developed a CF based model to integrate temporal cyclic patterns. Cheng et al.  [@Cheng_ijcai13] explored the temporal sequential patterns for personalized POI recommendation by using the transition probability of two successive check-ins of a user.

Existing studies has exploited spatial or temporal influences mainly using CF  [@Ye_sigir11; @Yuan_sigir13] and Markov transition approaches [@Cheng_ijcai13]. Due to the sparsity of users’ check-in records, it is hard to find similar users or calculates transition probability. Although matrix factorization (MF) methods are effective in dealing with the sparsity in user-POI matrix  [@Cheng_aaai12; @Lian_kdd14], they do not consider 