北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (1): 37-42,50.doi: 10.13190/j.jbupt.2017-081

• 论文 • 上一篇    下一篇

融合时空上下文信息的兴趣点推荐

徐前方1, 王嘉春1, 肖波1,2   

  1. 1. 北京邮电大学 信息与通信工程学院, 北京 100876;
    2. 无锡北邮感知技术产业研究院有限公司, 江苏 无锡 214135
  • 收稿日期:2017-05-12 出版日期:2018-02-28 发布日期:2018-01-04
  • 作者简介:徐前方(1975-),女,副教授,E-mail:xuqianfang@bupt.edu.cn

Point-of-Interest Recommendation with Spatio-Temporal Context Awareness

XU Qian-fang1, WANG Jia-chun1, XIAO Bo1,2   

  1. 1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Jiangsu Wuxi 214135, China
  • Received:2017-05-12 Online:2018-02-28 Published:2018-01-04

摘要: 为了给用户提供更好的位置服务,提出了一种位置社交网络中融入时空上下文信息的混合个性化兴趣点推荐模型.在空间上,对用户签到进行层次聚类,对各聚类内二维核密度估计的结果取平均.在时间上,利用用户签到的时间信息、签到的位置信息及社交网络构建转移矩阵,运行改进图的随机游走模型.混合模型融合时空上下文信息做推荐.在真实数据集上的实验结果表明,无论在标准推荐场景还是冷启动场景下,混合推荐模型的准确率和召回率性能均优于基准方法.

关键词: 位置社交网络, 时空上下文, 兴趣点推荐, 图的随机游走

Abstract: A personalized hybrid point-of-interest recommendation with spatio-temporal context awareness was proposed to provide users in location-based social networks with superior service. Spatially, two-dimension kernel density estimation was performed for each cluster of check-ins derived by hierarchical clustering and averaged. Meanwhile, random walk on graph was iterated on transition matrices generated from sequence information, location information and social network. The hybrid model combines spatio-temporal context above for recommendation. Experiment on real-world location-based social network(LBSN) datasets demonstrates that the performance metrics of precision and recall of the hybrid recommendation model is superior to other baseline techniques in both standard recommendation scene and cold-start scene.

Key words: location-based social networks, spatio-temporal context awareness, point-of-interest recommendation, random walk on graph

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