基于位置社交网络的个性化兴趣点推荐

Point-of-interest (POI) recommendation is an important service in location-based social networks (LBSNs) . For the current recommendation algorithm exists the problems of the noise data affects the recommended quality and low level of user personalization. Motived by this, this paper proposed a personalized joint recommendation algorithm (JRA) . JRA initially utilized the locality of user activity area to early filter the POIs which are impossible or less likely to be a result. For the received preliminary candidate set, then it also considered consider category factor and the popularity factor of POI, and the social behavior of the user to further improve the user experience. The experiments on real Foursquare check-in dataset demonstrate that the JRA compared with the current advanced algorithm, the accuracy rate increased by 11%, recall rate increased by 8%.