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Volume 35 Issue 3
May  2020
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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
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Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

  • 1 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China;
    2 Sino-German Institutes of Social Computing, Nanjing University, Nanjing 210023, China;
    3 State Grid Electric Power Research Institute, NARI Group Corporation, Nanjing 211000, China

Funds: This work was partially supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004704, the National Natural Science Foundation of China under Grant Nos. 61972196, 61832008, 61832005, the Key Research and Development Program of Jiangsu Province of China under Grant No. BE2018116, the open Project from the State Key Laboratory of Smart Grid Protection and Operation Control “Research on Smart Integration of Terminal-Edge-Cloud Techniques for Pervasive Internet of Things”, and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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  • Author Bio:

    Ming Chen received his B.S. and Ph.D. degrees in 2009 and 2019 respectively from Nanjing University, Nanjing, both in computer science. His research interests include big data, location-based services and data analysis in social networks. He is a student member of CCF.

  • Corresponding author:

    Wen-Zhong Li E-mail: lwz@nju.edu.cn

    Sang-Lu Lu E-mail: sanglu@nju.edu.cn

  • Received Date: December 05, 2018
  • Revised Date: January 08, 2020
  • Published Date: May 27, 2020
    Abstract
  • 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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    • References (26)
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