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Ming Chen, Wen-Zhong Li, Lin Qian, Sang-Lu Lu, Dao-Xu Chen. Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks[J]. Journal of Computer Science and Technology, 2020, 35(3): 603-616. DOI: 10.1007/s11390-020-9107-3
Citation: Ming Chen, Wen-Zhong Li, Lin Qian, Sang-Lu Lu, Dao-Xu Chen. Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks[J]. Journal of Computer Science and Technology, 2020, 35(3): 603-616. DOI: 10.1007/s11390-020-9107-3

Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

  • 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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