Abstract
Point-of-interest (POI) recommendations aim at identifying candidate POIs and ranking them in a descent order according to the probabilities of a user visiting them. The paper takes the scalability of information and user personalization into consideration to improve POI recommendation service, and proposes a personalized POI recommendation method based on user check-in behaviors in online social networks. First, the user’s travel experience in the target region is used to reduce the range of candidate POIs. At last, the proposed method ranks the candidate POIs to meet the user’s personalized need by combining the user preference, attraction of a POI on the target user, and social recommendations from friends. Experimental results show that the proposed method is feasible and effective.
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Zhou, E., Huang, J., Xu, X. (2015). A Point-of-Interest Recommendation Method Based on User Check-in Behaviors in Online Social Networks. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_14
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DOI: https://doi.org/10.1007/978-3-319-21786-4_14
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