Dual Fine-Granularity POI Recommendation on Location-Based Social Networks

Point of interest recommendation is a new form of popular recommendation in location-based social network (LBSN). Utilizing the rich information contained in the LBSN to do personalized recommendation can enhance user experience effectively and enhance user's dependence on LBSN. Facing the challenging problems in LBSN, such as no explicit user preferences, non-consistency of interest, the sparseness of data, and so on, a dual fine-granularity POI recommendation strategy is proposed, of which, on the one hand, the historical check-in information of each user is divided into 24 time periods in hours; on the other hand, each POI is divided into a number of potential topics and distribution. Both the information of user's check-in and comments are used to mine user's topic preference in different time periods for Top-N recommendation of the POIs. In order to achieve the recommendation ideas, first of all, according to the comments information on the visited POIs, we use LDA topic generation model to extract the topic distribution of each POI. Secondly, for each user, we divide each user's check-in data into 24 time periods, and connect it with the topic distribution of the corresponding POIs to map user interest preference on each topic in different periods. Finally, in order to solve the issue of data sparse, we use higher order singular value decomposition algorithm to decompose the third-order tensor of user-topic-time to get more accurate interest score of users on each topic in all time periods. The experiments on a real dataset show that the proposed approach outperforms the state-of-the-art POI recommendation methods.