  With the rapid growth of Location-Based Social Networks, personalized Points
of Interest (POIs) recommendation has become a critical task to help users
explore their surroundings. Due to the scarcity of check-in data, the
availability of geographical information offers an opportunity to improve the
accuracy of POI recommendation. Moreover, matrix factorization methods provide
effective models which can be used in POI recommendation. However, there are
two main challenges which should be addressed to improve the performance of POI
recommendation methods. First, leveraging geographical information to capture
both the user's personal, geographic profile and a location's geographic
popularity. Second, incorporating the geographical model into the matrix
factorization approaches. To address these problems, a POI recommendation
method is proposed in this paper based on a Local Geographical Model, which
considers both users' and locations' points of view. To this end, an effective
geographical model is proposed by considering the user's main region of
activity and the relevance of each location within that region. Then, the
proposed local geographical model is fused into the Logistic Matrix
Factorization to improve the accuracy of POI recommendation. Experimental
results on two well-known datasets demonstrate that the proposed approach
outperforms other state-of-the-art POI recommendation methods.
