A unified probabilistic factor model with social regularization for point of interest recommendation

Abstract

Point of Interest (POI) is an important task in Location Based Social Networks like, Gowalla, Foursquare and Brightkite etc as it guides user in decision making process. Although, ‘‘Probabilistic Factor Model’’ (PFM), is a promising method applied in various recommender systems, it is not effective in sparse data situation. Moreover, traditional recommendation methods do not consider the social feature of consumption, where a reliable friend suggests a popular item that does not pair with our preferences. This paper contributes a state-of-the-art solution for a personalized recommendation tool, which suggests interesting and new locations to users by bridging preference-aware and social-based recommendations. To demonstrate the effect of social context of user, the social regularization term, is unified into the Poisson Factor Model. Experiments are performed using the Gowalla and Brightkite dataset. The experiment reveals that, the new framework achieves higher accuracy than the traditional recommendation algorithm.