计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (5): 128-138.doi: 10.11896/jsjkx.240200099

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于地理时空关联和社会影响的兴趣点推荐

金红1,3, 陈礼珂1, 游兰1,3, 吕顺营2,4, 周开成1, 肖奎1,4   

  1. 1 湖北大学计算机学院 武汉 430062
    2 湖北大学网络空间安全学院 武汉 430062
    3 大数据智能分析与行业应用湖北省重点实验室(湖北大学) 武汉 430062
    4 智能感知系统与安全教育部重点实验室(湖北大学) 武汉 430062
  • 收稿日期:2024-02-26 修回日期:2024-07-29 出版日期:2025-05-15 发布日期:2025-05-12
  • 通讯作者: 吕顺营(823356045@qq.com)
  • 作者简介:(anya_1024@163.com)
  • 基金资助:
    国家自然科学基金(62377009);湖北省重点研发计划(2022BAA044);湖北省教育厅科学研究计划(Q20211010)

Point-of-interest Recommendation Based on Geospatial-TemporalCorrelations and Social Influence

JIN Hong1,3, CHEN Like1, YOU Lan1,3, LYU Shunying2,4, ZHOU Kaicheng1, XIAO Kui1,4   

  1. 1 School of Computer Science,Hubei University,Wuhan 430062,China
    2 School of Cyber Science and Technology,Hubei University,Wuhan 430062,China
    3 Hubei Key Laboratory of Big Data Intelligent Analysis and Application(Hubei University),Wuhan 430062,China
    4 Key Laboratory of Intelligent Sensing System and Security(Hubei University),Ministry of Education,Wuhan 430062,China
  • Received:2024-02-26 Revised:2024-07-29 Online:2025-05-15 Published:2025-05-12
  • About author:JIN Hong,born in 1984,Ph.D,lecturer.Her main research interests include graph computing,artificial intelligence,deep learning and complex network.
    LYU Shunying,born in 1965,M.S.His main research interests include compu-ter application technology,software engineering and spatio-temporal big data.
  • Supported by:
    National Natural Science Foundation of China(62377009),Key R & D Program of Hubei Province(2022BAA044) and Scientific Research Project of Education Department of Hubei Province(Q20211010).

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摘要： 随着基于位置的社交网络的流行,个性化兴趣点推荐已经成为一项重要任务。然而现有研究在对上下文信息建模及融合时对其内在联系考虑不充分,其中地理与时间两种上下文之间往往是相互影响的;此外,在建模用户社会关系时主要通过度量不同用户签到的POI子集之间的直接相似性来表达用户社交行为的相似性程度,未能更好地缓解数据稀疏对不同用户签到POI子集相似性度量的影响。因此,合理地重构了上下文信息模型并有效地融合建模到用户偏好中,提出了一种基于地理时空关联和社会影响的兴趣点推荐方法。该方法根据不同时间状态下用户的主要地理活动中心呈现空间聚集现象,使用带有时间约束的方法评估POI间的地理相关性,以建模POI地理信息对用户签到的影响。进一步地,在对用户社会关系建模时假设具有更多共同签到的POI或签到POI的类别有着更大重合度的用户社交行为的相似性更高,结合POI类别信息来提高社会关系建模的有效性和作用。最后,将提出的地理时空关联模型和用户社会关系模型融合到加权矩阵分解中,进行用户的个性化POI推荐。对比实验结果表明,所提方法具有更好的POI推荐性能,说明了提出的模型在上下文建模和克服数据稀疏性方面更具优势。

关键词: 基于位置的社交网络, 兴趣点推荐, 数据稀疏, 地理时空关联, 社会影响

Abstract: With the popularity of location-based social networks,personalized POI recommendation has become an important task.However,existing research inadequately considers the intrinsic relationships when modeling and integrating contextual information.Among these,geographical and temporal contexts often interact with and influence each other.Moreover,when mode-ling user social relationships,current approaches primarily measure the direct similarity between POI subsets visited by different users to express the similarity of their social behaviors.These approaches fails to effectively alleviate the impact of data sparsity on measuring the similarity of POI subsets visited by different users.Therefore,by reasonably reconstructing the contextual information model and effectively integrating it into user preference model,a POI recommendation method based on geospatial-temporal correlations and social influence is proposed.This method leverages the spatial aggregation phenomenon of users' primary geographic activity centers under different temporal states.It employs a time-constrained approach to assess the geographical correlations between POIs,thereby modeling the impact of POI geographical information on user check-ins.Furthermore,when mo-deling user social relationships,it is assumed that users with more shared check-ins at POIs or greater overlap in the categories of POIs they visit exhibit higher similarity in their social behaviors.By incorporating POI category information,the effectiveness and utility of social relationship modeling are enhanced.Finally,the proposed geospatial-temporal correlation model and user social relationship model are integrated into a weighted matrix factorization framework to perform personalized POI recommendations for users.Extensive experiments demonstrate that the proposed method achieves superior POI recommendation performance,highlighting the advantages of the proposed models in contextual modeling and overcoming data sparsity.

Key words: Location-based social network, Point-of-interest recommendation, Data sparse, Geospatial-temporal correlations, Social influence

中图分类号: 

  • TP391
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