计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (9): 139-144.doi: 10.11896/jsjkx.220900114

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于人群移动模式先验的兴趣点推荐

伊秋华1, 高浩然2, 陈馨琪3, 孔祥杰1   

  1. 1 浙江工业大学计算机科学与技术学院 杭州 310023
    2 大连理工大学软件学院 辽宁 大连 116000
    3 浙江大华技术股份有限公司 杭州 310051
  • 收稿日期:2022-09-11 修回日期:2022-11-27 出版日期:2023-09-15 发布日期:2023-09-01
  • 通讯作者: 孔祥杰(xjkong@ieee.org)
  • 作者简介:(qiuhuayi@outlook.com)
  • 基金资助:
    国家自然科学基金(62072409);浙江省自然科学基金(LR21F020003)

Human Mobility Pattern Prior Knowledge Based POI Recommendation

YI Qiuhua1, GAO Haoran2, CHEN Xinqi3, KONG Xiangjie1   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 School of Software,Dalian University of Technology,Dalian,Liaoning 116000,China
    3 Zhejiang Dahua Technology Co.,Ltd,Hangzhou 310051,China
  • Received:2022-09-11 Revised:2022-11-27 Online:2023-09-15 Published:2023-09-01
  • About author:YI Qiuhua,born in 2000,postgraduate,is a member of China Computer Federation.Her main research interests include urban data science,social computing and so on.
    KONG Xiangjie,born in 1981,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include network science,mobile computing and computational social science.
  • Supported by:
    National Natural Science Foundation of China(62072409) and Natural Science Foundation of Zhejiang Province,China(LR21F020003).

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摘要： 兴趣点推荐是基于位置的社交网络中的一项重要任务,为用户提供个性化的地点推荐。然而,当前的兴趣点推荐方法主要学习用户在兴趣点上的签到历史和用户间的社交关系网络,城市人群出行规律无法得到有效利用。首先提出了人群移动模式提取框架(Human Mobility Pattern Extraction,HMPE),利用图神经网络作为人群移动模式的提取器,引入注意力机制捕获城市交通模式的时空信息。HMPE通过制定下游任务,设计上采样模块将表征向量还原为任务目标,实现端到端的框架学习训练,完成人群移动模式提取器的预训练。其次,提出了兴趣点推荐算法HMRec(Human Mobility Recommendation),引入了人群移动模式的先验知识,使得推荐结果更符合城市中的人类出行意愿。对比实验结果显示,HMRec的表现优于基线模型。最后,讨论了兴趣点推荐存在的问题和未来的研究方向。

关键词: 兴趣点推荐, 人群移动模式, 图神经网络

Abstract: Point of interest(POI) recommendation is a fundamental task in location-based social networks,which provides users with personalized place recommendations.However,the current point of interest recommendation is mostly based on learning the user's check-in history at the point of interest in the social network and the user relationship network for recommendation,and the travel rules of urban crowds cannot be effectively used.To solve the above problem,firstly,a human mobility pattern extraction(HMPE) framework is proposed,which takes advantage of graph neural network to extract human mobility pattern.Then attention mechanism is introduced to capture the spatio-temporal information of urban traffic pattern.By establishing downstream tasks and designing upsampling modules,HMPE restores representation vectors to task objectives.An end-to-end framework is built to complete pre-training of human mobility pattern extraction module.Secondly,the human mobility tecommendation(HMRec)algorithm is proposed,which introduces the prior knowledge of crowd movement patterns,so that the recommendation results are more in line with human travel intentions in cities.Extensive experiments show that HMRec is superior to baseline mo-dels.Finally,the existing problems and future research directions of interest point recommendation are discussed.

Key words: POI recommendation, Human mobility pattern, Graph neural network

中图分类号: 

  • TP311
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