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基于簽到活躍度和時空概率模型的自適應興趣點推薦方法

司亞利,  張付志,  劉文遠

司亞利, 張付志, 劉文遠. 基于簽到活躍度和時空概率模型的自適應興趣點推薦方法[J]. 電子與信息學報, 2020, 42(3): 678-686. doi: 10.11999/JEIT190287
引用本文: 司亞利, 張付志, 劉文遠. 基于簽到活躍度和時空概率模型的自適應興趣點推薦方法[J]. 電子與信息學報, 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

基于簽到活躍度和時空概率模型的自適應興趣點推薦方法

doi: 10.11999/JEIT190287
  • 1.

    燕山大學信息科學與工程學院 秦皇島 066004

  • 2.

    燕山大學里仁學院 秦皇島 066004

  • 3.

    河北省計算機虛擬技術與系統(tǒng)集成重點實驗室(燕山大學) 秦皇島 066004

基金項目: 國家自然科學基金(61379116, 61772452)，河北省自然科學基金(F2015203046, F2015501105)
詳細信息
    作者簡介:

    司亞利：女，1981年生，副教授，研究方向為興趣點推薦系統(tǒng)

    張付志：男，1964年生，教授，研究方向為推薦系統(tǒng)

    劉文遠：男，1968年生，教授，研究方向為物聯(lián)網系統(tǒng)

    通訊作者:

    張付志　xjzfz@ysu.edu.cn

  • 中圖分類號: TP391

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

  • 1.

    School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

  • 2.

    School of Liren, Yanshan University, Qinhuangdao 066004, China

  • 3.

    Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province (Yanshan University), Qinhuangdao 066004, China

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

    針對現(xiàn)有興趣點(POI)推薦算法對不同簽到特征的用戶缺乏自適應性問題，該文提出一種基于用戶簽到活躍度(UCA)特征和時空(TS)概率模型的自適應興趣點推薦方法UCA-TS。利用概率統(tǒng)計分析方法提取用戶簽到的活躍度特征，給出一種用戶不活躍和活躍的隸屬度計算方法。在此基礎上，分別采用結合時間因素的1維冪律函數和2維高斯核密度估計來計算不活躍和活躍特征的概率值，同時融入興趣點流行度來進行推薦。該方法能自適應用戶的簽到特征，并能更準確體現(xiàn)用戶簽到的時間和空間偏好。實驗結果表明，該方法能夠有效提高推薦精度和召回率。

    Abstract:

    Existing Point-Of-Interest (POI) recommendation algorithms lack adaptability for users with different check-in features. To solve this problem, an adaptive POI recommendation method UCA-TS based on User Check-in Activity (UCA) feature and Temporal-Spatial (TS) probabilistic models is proposed. The user check-in activity is extracted using a probabilistic statistical analysis method, and a calculation method of user's inactive and active membership is given. On this basis, one-dimensional power law function and two-dimensional Gaussian kernel density estimation combined with time factor are used to calculate the probability for inactive and active features respectively, and the popularity of POI is incorporated to recommend. This method can adapt to the users' check-in features and reflect the users' check-in temporal-spatial preferences more accurately. The experiments show that the proposed method can effectively improve the recommendation precision and recall.

    • HTML全文

  • 圖  1  兩個數據集中UCN和相應用戶數量統(tǒng)計圖

    圖  2  用戶簽到數量的概率質量函數圖

    圖  3  k對精度和召回率的影響

    圖  4  1維和2維模型對Foursquare數據集中不活躍用戶和活躍用戶的精度和召回率

    圖  5  興趣點推薦算法在兩個數據集上的精度和召回率

    表  1  自適應興趣點推薦算法(UCA-TS)

    　輸入：簽到數據集UCall，推薦的目標用戶u，時間槽t;(11)　for each l∈L-Lu do
    　輸出：推薦的top-n興趣點(12)　　p1(l|Lu,t’)←1; p2(l|Lu,t’)←0;
    　(1) 使用式(1)計算UCNu，式(3)計算Tu;(13)　　for each li∈Lu,t’ do
    　(2) k←0; 初始化C={c1, c2}和A={aij};(14)　　　計算dli,l和p(l|li);
    　(3) repeat(15)　　　計算 p1(l|Lu,t’)←p1(l|Lu,t’)×p(l|li);
    　(4)　k←k+1;(16)　　　計算 p2(l|Lu,t’);
    　(5)　更新聚類中心C(k)和A(k-1);(17)　　end for
    　(6)　更新隸屬度矩陣A(k)和聚類中心C(k);(18)　　使用式(20)計算pt(l);
    　(7) until 式(5)收斂(19)　end for
    　(8) 返回au={a1u, a2u};(20) end for
    　(9) for t’=0 to 23 do(21) 使用式(22)計算Pu,t,l;
    　(10)　使用式(21)計算wt’-t; 使用式(15)—式(17)計算H;(22) 排序Pu,t,l并返回top-n興趣點。
    下載: 導出CSV

    表  2  LBSNs簽到數據集的統(tǒng)計情況

    數據集簽到數興趣點數用戶數每個用戶訪問地點平均數每個地點的平均訪問數每個用戶平均簽到數簽到密度
    Foursquare194108559623214619840.0149
    Gowalla45690524236101623013450.0019
    下載: 導出CSV

    表  3  Foursquare和Gowalla數據集的不活躍和活躍用戶數據統(tǒng)計

    數據集用戶類別用戶數量簽到記錄總數平均簽到記錄數平均簽到時間槽數平均簽到地點數
    Foursquare不活躍用戶190888181461228
    活躍用戶41310592725619114
    Gowalla不活躍用戶975630462131921
    活躍用戶40615228437518204
    下載: 導出CSV

    表  4  興趣點推薦算法在兩個數據集上的Fβ指標值(β=1)

    數據集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
    下載: 導出CSV
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  • 收稿日期:  2019-04-25
  • 修回日期:  2019-10-29
  • 網絡出版日期:  2019-11-11
  • 刊出日期:  2020-03-19

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