Abstract
Recommending the next point-of-interest (POI) to mobile users is an interesting topic for mobile social networks to provide personalized location-based services. In this paper, we propose an interest-aware next POI recommendation approach, which consider the location interest among similar users and the contextual information (such as time, current location, and friends preference) for POI recommendation. We develop a spatial-temporal topic model to describe users location interest, based on which we form comprehensive feature representations regarding user interest and contextual information. We propose a supervised learning prediction model for next POI recommendation. Experiments based on the Gowalla dataset verify the accuracy and efficiency of the proposed approach.
This work was partially supported by the National Key R&D Program of China (Grant No. 2017YFB1001801), the National Natural Science Foundation of China (Grant Nos. 61672278, 61373128, 61321491), the science and technology project from State Grid Corporation of China (Contract No. SGSNXT00YJJS1800031), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Sino-German Institutes of Social Computing.
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Chen, M., Li, W., Qian, L., Lu, S., Chen, D. (2018). Interest-Aware Next POI Recommendation for Mobile Social Networks. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_3
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DOI: https://doi.org/10.1007/978-3-319-94268-1_3
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