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基于签到活跃度和时空概率模型的自适应兴趣点推荐方法

司亚利 张付志 刘文远

司亚利, 张付志, 刘文远. 基于签到活跃度和时空概率模型的自适应兴趣点推荐方法[J]. 电子与信息学报, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287
引用本文: 司亚利, 张付志, 刘文远. 基于签到活跃度和时空概率模型的自适应兴趣点推荐方法[J]. 电子与信息学报, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287
Yali SI, Fuzhi ZHANG, Wenyuan LIU. An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models[J]. Journal of Electronics & Information Technology, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287
Citation: Yali SI, Fuzhi ZHANG, Wenyuan LIU. An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models[J]. Journal of Electronics & Information Technology, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287

基于签到活跃度和时空概率模型的自适应兴趣点推荐方法

doi: 10.11999/JEIT190287
基金项目: 国家自然科学基金(61379116, 61772452),河北省自然科学基金(F2015203046, F2015501105)
详细信息
    作者简介:

    司亚利:女,1981年生,副教授,研究方向为兴趣点推荐系统

    张付志:男,1964年生,教授,研究方向为推荐系统

    刘文远:男,1968年生,教授,研究方向为物联网系统

    通讯作者:

    张付志 xjzfz@ysu.edu.cn

  • 中图分类号: TP391

An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models

Funds: The National Natural Science Foundation of China (61379116, 61772452), The Natural Science Foundation of Hebei Province (F2015203046, F2015501105)
  • 摘要:

    针对现有兴趣点(POI)推荐算法对不同签到特征的用户缺乏自适应性问题,该文提出一种基于用户签到活跃度(UCA)特征和时空(TS)概率模型的自适应兴趣点推荐方法UCA-TS。利用概率统计分析方法提取用户签到的活跃度特征,给出一种用户不活跃和活跃的隶属度计算方法。在此基础上,分别采用结合时间因素的1维幂律函数和2维高斯核密度估计来计算不活跃和活跃特征的概率值,同时融入兴趣点流行度来进行推荐。该方法能自适应用户的签到特征,并能更准确体现用户签到的时间和空间偏好。实验结果表明,该方法能够有效提高推荐精度和召回率。

  • 图  1  两个数据集中UCN和相应用户数量统计图

    图  2  用户签到数量的概率质量函数图

    图  3  k对精度和召回率的影响

    图  4  1维和2维模型对Foursquare数据集中不活跃用户和活跃用户的精度和召回率

    图  5  兴趣点推荐算法在两个数据集上的精度和召回率

    表  1  自适应兴趣点推荐算法(UCA-TS)

     输入:签到数据集UCall,推荐的目标用户u,时间槽t;(11) for each lL-Lu do
     输出:推荐的top-n兴趣点(12)  p1(l|Lu,t)←1; p2(l|Lu,t)←0;
     (1) 使用式(1)计算UCNu,式(3)计算Tu;(13)  for each liLu,t do
     (2) k←0; 初始化C={c1, c2}和A={aij};(14)   计算dli,lp(l|li);
     (3) repeat(15)   计算 p1(l|Lu,t)←p1(l|Lu,tp(l|li);
     (4) kk+1;(16)   计算 p2(l|Lu,t);
     (5) 更新聚类中心C(k)和A(k-1);(17)  end for
     (6) 更新隶属度矩阵A(k)和聚类中心C(k);(18)  使用式(20)计算pt(l);
     (7) until 式(5)收敛(19) end for
     (8) 返回au={a1u, a2u};(20) end for
     (9) for t’=0 to 23 do(21) 使用式(22)计算Pu,t,l;
     (10) 使用式(21)计算wt’-t; 使用式(15)—式(17)计算H;(22) 排序Pu,t,l并返回top-n兴趣点。
    下载: 导出CSV

    表  2  LBSNs签到数据集的统计情况

    数据集签到数兴趣点数用户数每个用户访问地点平均数每个地点的平均访问数每个用户平均签到数签到密度
    Foursquare194108559623214619840.0149
    Gowalla45690524236101623013450.0019
    下载: 导出CSV

    表  3  Foursquare和Gowalla数据集的不活跃和活跃用户数据统计

    数据集用户类别用户数量签到记录总数平均签到记录数平均签到时间槽数平均签到地点数
    Foursquare不活跃用户190888181461228
    活跃用户41310592725619114
    Gowalla不活跃用户975630462131921
    活跃用户40615228437518204
    下载: 导出CSV

    表  4  兴趣点推荐算法在两个数据集上的Fβ指标值(β=1)

    数据集Top-nSKUTE+SETPR+UMSAMMUCA-TS
    Foursquaretop-50.04130.04350.05550.06640.0749
    top-100.03440.03560.04570.05970.0669
    top-200.02450.02580.03980.05200.0571
    Gowallatop-50.03990.04250.04660.05920.0663
    top-100.03130.03280.03940.05510.0602
    top-200.02190.02290.02830.04160.0450
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-04-25
  • 修回日期:  2019-10-29
  • 网络出版日期:  2019-11-11
  • 刊出日期:  2020-03-19

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