Abstract
Point-of-interest (POI) recommendation on location-based social network (LBSN) is an important service in mobile environment. POI recommendation recommends places that users have not visited before. In this paper, we introduce time slot to describe the feature vector of locations. We consider that the locations that user has visited may reflect user's preference. Hence, we calculate the similarity between the visited locations and the unvisited locations. Meanwhile, we consider the influence of the physical distance and the weight of the visited locations. We conduct an experiment and the experimental results show that our method has better precision and recall than the other two methods.
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