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With the rapid growth of Location-Based Social Networks, personalized Pointsof Interest (POIs) recommendation has become a critical task to help usersexplore their surroundings. Due to the scarcity of check-in data, theavailability of geographical information offers an opportunity to improve theaccuracy of POI recommendation. Moreover, matrix factorization methods provideeffective models which can be used in POI recommendation. However, there aretwo main challenges which should be addressed to improve the performance of POIrecommendation methods. First, leveraging geographical information to captureboth the user's personal, geographic profile and a location's geographicpopularity. Second, incorporating the geographical model into the matrixfactorization approaches. To address these problems, a POI recommendationmethod is proposed in this paper based on a Local Geographical Model, whichconsiders both users' and locations' points of view. To this end, an effectivegeographical model is proposed by considering the user's main region ofactivity and the relevance of each location within that region. Then, theproposed local geographical model is fused into the Logistic MatrixFactorization to improve the accuracy of POI recommendation. Experimentalresults on two well-known datasets demonstrate that the proposed approachoutperforms other state-of-the-art POI recommendation methods.

LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation