The Journal of Machine Learning Research
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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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.