Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (9): 139-144.doi: 10.11896/jsjkx.220900114

• Database & Big Data & Data Science • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP311
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