Cross-region collaborative filtering for new point-of-interest recommendation

With the rapid growth of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation is in increasingly higher demand these years. In this paper, our aim is to recommend new POIs to a user in regions where he has rarely been before. Different from the classical memory-based recommendation algorithms using user rating data to compute similarity between users or items to make recommendation, we propose a cross-region collaborative filtering method based on hidden topics mined from user check-in records to recommend new POIs. Experimental results on a real-world LBSNs dataset show that our method consistently outperforms naive CF method.