PRPOIR: Exploiting the Region-Level Interest for POI Recommendation

Point of interest (POI) recommendation has become an important means to help people discover attractive locations. Previous studies show that modeling the context information of users' check-ins is necessary for POI recommendation. However, most previous hybrid models ignore the fact that a user's interested region is likely to drive him to visit other POIs in it, which means that his region-level interest is indispensable in the recommendation process. In this paper, we take this challenge and propose a hybrid model called POI-Region POI Recommendation (PRPOIR). Specifically, PRPOIR contains two modules: Interest Module and Context Module. In Interest Module, inspired by the success of the Logistic Matrix Factorization (LMF) to model implicit feedback, we apply it to model users' POI-level interest and region-level interest respectively. To capture the difference in the users' emphasis on region-level interests, we innovatively introduce a strategy to establish the dynamic weight of users' region-level interest. Then in Context Module, by further integrating geographical influence and social correlations into PRPOIR, it can alleviate the sparsity of check-ins and bring further performance improvement. To verify the effectiveness of PRPOIR, we conduct experiments on two real-world datasets, experimental results show that PRPOIR outperforms the state-of-the-art models.