Location Regularization-Based POI Recommendation in Location-Based Social Networks

POI (point-of-interest) recommendation as one of the efficient information filtering techniques has been widely utilized in helping people find places they are likely to visit, and many related methods have been proposed. Although the methods that exploit geographical information for POI recommendation have been studied, few of these studies have addressed the implicit feedback problem. In fact, in most location-based social networks, the user’s negative preferences are not explicitly observable. Consequently, it is inappropriate to treat POI recommendation as traditional recommendation problem. Moreover, previous studies mainly explore the geographical information from a user perspective and the methods that model them from a location perspective are not well explored. Hence, this work concentrates on exploiting the geographical characteristics from a location perspective for implicit feedback, where a neighborhood aware Bayesian personalized ranking method (NBPR) is proposed. To be specific, the weighted Bayesian framework that was proposed for personalized ranking is first introduced as our basic POI recommendation method. To exploit the geographical characteristics from a location perspective, we then constrain the ranking loss by using a regularization term derived from locations, and assume nearest neighboring POIs are more inclined to be visited by similar users. Finally, several experiments are conducted on two real-world social networks to evaluate the NBPR method, where we can find that our NBPR method has better performance than other related recommendation algorithms. This result also demonstrates the effectiveness of our method with neighborhood information and the importance of the geographical characteristics