计算机科学 ›› 2023, Vol. 50 ›› Issue (12): 113-122.doi: 10.11896/jsjkx.230200105

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于时间聚类和用户动态相似度的自适应位置推荐算法

朱俊1,2, 韩立新2, 宗平2, 刘红英3, 谢玲3, 李景仙2   

  1. 1 南京工业职业技术大学计算机与软件学院 南京 210023
    2 河海大学计算机与信息学院 南京 211100
    3 南京理工大学紫金学院计算机学院 南京 210023
  • 收稿日期:2023-02-16 修回日期:2023-05-23 出版日期:2023-12-15 发布日期:2023-12-07
  • 通讯作者: 朱俊(zj_zijin@163.com)
  • 基金资助:
    国家自然科学基金(41771251);江苏省高校自然科学研究项目(21KJB520009);南京工业职业技术大学引进人才科研启动基金(YK23-05-01)

Adaptive Location Recommendation Based on Time Slots Clustering and User Dynamic Similarity

ZHU Jun1,2, HAN Lixin2, ZONG Ping2, LIU Hongying3, XIE Ling3, LI Jingxian2   

  1. 1 School of Computer and Software,Nanjing Vocational University of Industry Technology,Nanjing 210023,China
    2 College of Computer and Information Engineering,Hohai University,Nanjing 211100,China
    3 College of Computer Science,Nanjing University of Science and Technology Zijin College,Nanjing 210023,China
  • Received:2023-02-16 Revised:2023-05-23 Online:2023-12-15 Published:2023-12-07
  • About author:ZHU Jun,born in 1987,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include machine learning and recommender systems.
  • Supported by:
    National Natural Science Foundation of China(41771251),Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(21KJB520009) and Start-up Fund for New Talented Researchers of Nanjing Vocational University of Industry Technology(YK23-05-01).

摘要: 位置推荐是位置社交网络中为商家和用户提供的一项重要服务,推荐结果易受用户上下文和时空上下文影响。针对当前研究忽略了用户的动态相似度、推荐模型自适应性较弱以及存在严重的数据稀疏问题,提出了一种基于时间聚类和用户动态相似度的自适应位置推荐算法(ALRTU)。首先,基于时间槽的签到数据统计特征,对时间进行模糊C均值聚类,提取聚类内的时间相似度,利用平滑技术更新原始评分矩阵,以解决数据稀疏问题。分别计算用户在不同时间槽的动态相似度,根据目标时间段所属的时间聚类自适应选择不同的评分数据集,完成用户偏好和时间特征挖掘。其次,根据用户的访问频率特征,为活跃用户和非活跃用户自适应选择核密度估计或幂律分布模型,完成地理特征挖掘。最后,融合用户、时间和空间上下文的综合影响完成位置推荐。在两个真实的位置社交网络数据集Brightkite和Gowalla中开展准确度评估实验,实验结果表明,与基准方法中最高的推荐精度相比,ALRTU算法在Brightkite和Gowalla数据集中的准确度仍分别平均提高了3.74%和1.42%。

关键词: 位置推荐, 自适应推荐, 时间聚类, 动态相似度, 空间特征

Abstract: Location recommendation is an important service for businesses and users in location-based social networks,and the recommended results are greatly influenced by user preferences and spatial-temporal contexts.Most existing researches ignore the variation in user similarity over time,lack adaptability when making recommendations,and suffer from serious data sparsity pro-blem.To address the issues above,this paper proposes an adaptive location recommendation algorithm(ALRTU) based on time slots clustering and user dynamic similarity.First,time slots clustering based on fuzzy c-means is devised according to the statistics of historical check-in data.Time similarity in each time cluster is calculated,and original ratings are updated further by app-lying smoothing technology to solve the problem of data sparsity.The dynamic similarities of users are calculated by hour.Diffe-rent rating subsets are selected adaptively according to time slots clustering and then realize user preferences and temporal influences mining.Second,users are classified on the basis of check-in frequency,and then kernel density estimation or power law distribution algorithm is selected adaptively to mine geographical features.Finally,user preferences and spatial-temporal contexts are effectively fused to produce location recommendations.Extensive offline experiments are conducted on two real-world datasets(Brightkite and Gowalla) to verify the accuracy.Experimental results show that the accuracy of ALRTU on Brightkite and Gowalla datasets is respectively improved by 3.74% and 1.42% on average,compared with the highest recommendation accuracy among the benchmark methods.

Key words: Location recommendation, Adaptive recommendation, Time slots clustering, Dynamic similarity, Spatial features

中图分类号: 

  • TP181
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