计算机科学技术学报 ›› 2020,Vol. 35 ›› Issue (3): 603-616.doi: 10.1007/s11390-020-9107-3

所属专题: Artificial Intelligence and Pattern Recognition Data Management and Data Mining

• Artificial Intelligence and Pattern Recognition • 上一篇    下一篇

基于循环神经网络位置兴趣挖掘的下一跳兴趣点推荐

Ming Chen1, Student Member, CCF, Wen-Zhong Li1,2,*, Member, CCF, ACM, IEEE, Lin Qian1,3, Member, CCF Sang-Lu Lu1,2,*, Member, CCF, ACM, IEEE, Dao-Xu Chen1, Senior Member, CCF, Member, ACM, IEEE   

  1. 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
  • 收稿日期:2018-12-06 修回日期:2020-01-09 出版日期:2020-05-28 发布日期:2020-05-28
  • 通讯作者: Wen-Zhong, Sang-Lu Lu E-mail:lwz@nju.edu.cn;sanglu@nju.edu.cn
  • 作者简介: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.
  • 基金资助:
    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.

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

Ming Chen1, Student Member, CCF, Wen-Zhong Li1,2,*, Member, CCF, ACM, IEEE, Lin Qian1,3, Member, CCF Sang-Lu Lu1,2,*, Member, CCF, ACM, IEEE, Dao-Xu Chen1, Senior Member, CCF, Member, ACM, IEEE        

  1. 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
  • Received:2018-12-06 Revised:2020-01-09 Online:2020-05-28 Published:2020-05-28
  • Contact: Wen-Zhong, Sang-Lu Lu E-mail:lwz@nju.edu.cn;sanglu@nju.edu.cn
  • About author: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.
  • Supported by:
    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.

在移动社交网络中,下一跳兴趣点 (POI) 推荐是一个非常重要的问题,它可以为移动用户提供基于位置的个性化服务。在本文中,我们提出了一种基于循环神经网络 (RNN) 的下一跳 POI 推荐方法,该方法既考虑了类似用户的位置兴趣,也考虑了上下文信息(如时间、当前位置和朋友的偏好)。我们提出了一个时空主题模型来描述用户的位置兴趣,在此基础上,我们将用户兴趣和上下文信息特征综合表示。我们为下一跳 POI 推荐提出了一个受监督的 RNN 学习预测模型。实验结果显示,通过挖掘用户的位置兴趣,可有效为用户提供下一跳位置推荐。基于 Gowalla 数据集和布莱特凯特数据集的实验验证了所提出方法的准确性和效率,其F1-socore 分别达到0.19和0.35, 超过现有的基准算法。未来的研究可考虑挖掘更丰富的上下文信息以及在多种场景下的使用。

关键词: 位置兴趣, 基于位置服务, 下一跳兴趣点推荐, 社交网络

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.

Key words: location interest, location-based service, point-of-interest (POI) recommendation, mobile social network

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