期刊
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
卷 35, 期 3, 页码 603-616出版社
SCIENCE PRESS
DOI: 10.1007/s11390-020-9107-3
关键词
location interest; location-based service; point-of-interest (POI) recommendation; mobile social network
资金
- National Key Research and Development Program of China [2018YFB1004704]
- National Natural Science Foundation of China [61972196, 61832008, 61832005]
- Key Research and Development Program of Jiangsu Province of China [BE2018116]
- State Key Laboratory of Smart Grid Protection and Operation Control Research on Smart Integration of Terminal-Edge-Cloud Techniques for Pervasive Internet of Things
- Collaborative Innovation Center of Novel Software Technology and Industrialization
In mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based services for mobile users. In this paper, we propose a recurrent neural network (RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information (such as time, current location, and friends' preferences). We develop a spatial-temporal topic model to describe users' location interest, based on which we form comprehensive feature representations of user interests and contextual information. We propose a supervised RNN learning prediction model for next POI recommendation. Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach, and achieve best F1-score of 0.196 754 on the Gowalla dataset and 0.354 592 on the Brightkite dataset.
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