With the rapid growth of location-based social network (LBSN), point-of-interest (POI) recommendation has become an important means to meet users’ personalized demands and alleviate the information overload problem. However, traditional POI recommendation algorithms have the following problems: 1)most of existing POI recommendation algorithms simplify users’ check-in frequencies at a location, i.e., regardless how many times a user checks-in a location, they only use binary values to indicate whether a user has visited a POI; 2)matrix factorization based POI recommendation algorithms totally treat users’ check-in frequencies as ratings in traditional recommender systems and model users’ check-in behaviors using the Gaussian distribution; 3)traditional POI recommendation algorithms ignore that users’ check-in feedback is implicit and only positive examples are observed in POI recommendation. In this paper, we propose a ranking based Poisson matrix factorization model for POI recommendation. Specifically, we first utilize the Poisson distribution instead of the Gaussian distribution to model users’ check-in behaviors. Then, we use the Bayesian personalized ranking metric to optimize the loss objective function of Poisson matrix factorization and fit the partial order of POIs. Finally, we leverage a regularized term encoding geographical influence to constrain the process of Poisson matrix factorization. Experimental results on real-world datasets show that our proposed approach outperforms traditional POI recommendation algorithms.