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复杂网络环境下基于信任传递的推荐模型研究

李慧 马小平 施珺 李存华 仲兆满 蔡虹

李慧, 马小平, 施珺, 李存华, 仲兆满, 蔡虹. 复杂网络环境下基于信任传递的推荐模型研究. 自动化学报, 2018, 44(2): 363-376. doi: 10.16383/j.aas.2018.c160395
引用本文: 李慧, 马小平, 施珺, 李存华, 仲兆满, 蔡虹. 复杂网络环境下基于信任传递的推荐模型研究. 自动化学报, 2018, 44(2): 363-376. doi: 10.16383/j.aas.2018.c160395
LI Hui, MA Xiao-Ping, SHI Jun, LI Cun-Hua, ZHONG Zhao-Man, CAI Hong. A Recommendation Model by Means of Trust Transition in Complex Network Environment. ACTA AUTOMATICA SINICA, 2018, 44(2): 363-376. doi: 10.16383/j.aas.2018.c160395
Citation: LI Hui, MA Xiao-Ping, SHI Jun, LI Cun-Hua, ZHONG Zhao-Man, CAI Hong. A Recommendation Model by Means of Trust Transition in Complex Network Environment. ACTA AUTOMATICA SINICA, 2018, 44(2): 363-376. doi: 10.16383/j.aas.2018.c160395

复杂网络环境下基于信任传递的推荐模型研究

doi: 10.16383/j.aas.2018.c160395
基金项目: 

连云港市科技计划项目 CG1315

国家自然科学基金 61403155

连云港市科技计划项目 CXY1530

连云港市科技计划项目 SH1507

国家自然科学基金 61403156

淮海工学院科研基金资助项目 Z2017012

淮海工学院科研基金资助项目 Z2017012, Z2015012

连云港市科技计划项目 CG1413

江苏高校品牌专业建设工程资助项目 PPZY2015A038

详细信息
    作者简介:

    李慧  博士, 淮海工学院计算机工程学院副教授.主要研究方向为个性化推荐, 社会网络分析.E-mail:shufanzs@126.com

    施珺  淮海工学院计算机工程学院教授.主要研究方向为智能信息处理.E-mail:sj_lfg@hotmail.com

    李存华  博士, 淮海工学院计算机工程学院教授.主要研究方向为数据挖掘.E-mail:cli2000@126.com

    仲兆满  博士, 淮海工学院计算机工程学院副教授.主要研究方向为中文信息处理.E-mail:zhongzhaoman@163.com

    蔡虹  淮海工学院计算机工程学院讲师.主要研究方向为智能信息处理.E-mail:caihong@263.net

    通讯作者:

    马小平  博士, 中国矿业大学信电学院教授.主要研究方向为智能计算.本文通信作者.E-mail:xpma@cumt.edu.cn

A Recommendation Model by Means of Trust Transition in Complex Network Environment

Funds: 

Science and Technology Planning Project of Lianyungang CG1315

National Natural Science Foundation of China 61403155

Science and Technology Planning Project of Lianyungang CXY1530

Science and Technology Planning Project of Lianyungang SH1507

National Natural Science Foundation of China 61403156

Science Foundation of Huaihai Institute of Technology Z2017012

Science Foundation of Huaihai Institute of Technology Z2017012, Z2015012

Science and Technology Planning Project of Lianyungang CG1413

Topnotch Academic Programs Project of Jiangsu Higher Education Institutions PPZY2015A038

More Information
    Author Bio:

     Ph. D., associate professor in the Department of Computer Science, Huaihai Institute of Technology. Her research interest covers personality recommendation and socila network analysis

     Professor in the Department of Computer Science, Huaihai Institute of Technology. Her main research interest is information processing

     Ph. D., professor is the Department of Computer Science, Huaihai Institute of Technology. His main research interest is data mining

     Ph. D., associate professor in the Department of Computer Science, Huaihai Institute of Technology. His main research interest is Chinese information processing

     Lecturer in the Department of Computer Science, Huaihai Institute of Technology. Her main research interest is information processing

    Corresponding author: MA Xiao-Pin  Ph. D., professor at the School of Information & Electrical Engineering, China University of Mining & Technology. His main research interests is intelligent computing. Corresponding author of this paper
  • 摘要: 针对推荐系统中普遍存在的数据稀疏和冷启动等问题,本文结合用户自身评分与用户的社会信任关系构建推荐模型,提出了一种基于信任关系传递的社会网络推荐算法(Trust transition recommendation model,TTRM).该方法首先通过计算信任网络中节点的声望值与偏见值来发现信任网络中的不可信节点,并通过对其评分权重进行弱化来减轻其对信任网络产生的负面影响.其次,算法又利用朋友的信任矩阵对用户自身的特征向量进行修正,解决了用户特征向量的精准构建及信任传递问题.同时为了实现修正误差的最小化,算法利用推荐特性进行用户相似度计算并通过带有社会正则化约束的矩阵分解技术实现社会网络推荐.实验结果表明,TTRM算法较传统的社会网络推荐算法在性能上具有显著提高.
    1)  本文责任编委 赵铁军
  • 图  1  带有信任度的社会网络示例

    Fig.  1  A example of social network with trust weights

    图  2  基于用户信任关系推荐的概率图模型

    Fig.  2  Graphic model for recommendation based on trust relationship

    图  3  参数$\theta$的取值验证实验

    Fig.  3  Verification experiment of parameters $\theta$

    图  4  Slashdot数据集的入度平均值与声望值

    Fig.  4  In-degree mean and deserve for Slashdot datasets

    图  5  不同维度与不同训练集比例下参数$\lambda_B$的MAE结果

    Fig.  5  Effect experiment of parameter $\lambda_B$ under different training percent

    图  6  参数$\alpha$的影响实验(维度$=10$)

    Fig.  6  Impact experiment of parameter (dimensionality$=10$)

    表  1  图 1中各节点最终的偏见值与声望值

    Table  1  Final bias and prestige values for the nodes in Fig. 1

    节点1 节点2 节点3
    偏见值(Bias) 0.13 0.08 -0.14
    声望值(Prestige) -0.33 0.73 -1.00
    下载: 导出CSV

    表  2  图 1中每次迭代各节点的偏见值与声望值

    Table  2  Bias and prestige values after each iteration in Fig. 1

    迭代次数 节点1 节点2 节点3
    偏见值 声望值 偏见值 声望值 偏见值 声望值
    0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00
    1 0.10 0.00 -0.25 0.8 -0.25 0.00
    2 0.12 -0.30 0.01 0.75 -0.16 -0.75
    3 0.13 -0.33 0.08 0.73 -0.15 -1.00
    4 0.13 -0.33 0.08 0.73 -0.14 -1.00
    5 0.13 -0.33 0.08 0.73 -0.14 -1.00
    下载: 导出CSV

    表  3  不同用户评价数量下各推荐算法对比结果

    Table  3  Comparative results of different algorithms under different user evaluation number

    评价数量 0 1~5 6~10 11~20 21~40 4~80 81~160 161~320 321~640 $>640$
    90% Trust 1.79 1.25 1.08 1.02 1.00 0.98 0.95 0.92 0.90 0.88
    STE 1.69 1.18 1.03 1.00 0.99 0.97 0.95 0.92 0.90 0.88
    SocialMF 1.32 1.12 1.01 0.98 0.93 0.91 0.90 0.88 0.86 0.85
    TTRM 1.07 1.02 0.95 0.90 0.88 0.86 0.85 0.84 0.83 0.82
    80% Trust 1.85 1.28 1.09 1.04 1.02 0.99 0.97 0.95 0.92 0.90
    STE 1.68 1.15 1.05 1.03 1.00 0.96 0.95 0.92 0.91 0.89
    SocialMF 1.23 1.10 1.02 0.98 0.97 0.94 0.93 0.91 0.90 0.88
    TTRM 1.09 1.05 0.95 0.94 0.93 0.92 0.91 0.90 0.88 0.86
    50% Trust 1.89 1.32 1.13 1.09 1.06 1.03 1.01 0.99 0.94 0.92
    STE 1.75 1.25 1.10 1.07 1.03 0.99 0.97 0.95 0.88 0.86
    SocialMF 1.35 1.15 1.08 1.02 1.00 0.98 0.95 0.93 0.85 0.85
    TTRM 1.12 1.10 0.98 0.97 0.94 0.93 0.92 0.91 0.89 0.85
    20% Trust 1.98 1.30 1.13 1.12 1.11 1.11 1.09 1.04 1.04 1.03
    STE 1.78 1.23 1.10 1.08 1.07 1.06 1.05 1.02 1.02 1.01
    SocialMF 1.42 1.21 1.09 1.05 1.01 1.02 0.98 0.94 0.89 0.89
    TTRM 1.13 1.10 1.00 0.98 0.97 0.96 0.95 0.94 0.88 0.88
    下载: 导出CSV

    表  4  各推荐算法的性能对比结果

    Table  4  Performance comparison results of different recommendation algorithm

    UserMean ItemMean PMF NMF Trust SocialMF TTRM
    90% MAE 0.913 0.877 0.865 0.871 0.832 0.802 0.789
    RMSE 1.169 1.238 1.154 1.162 1.101 1.051 1.021
    URMSE 1.740 1.652 1.156 1.142 1.132 0.937 0.902
    80% MAE 0.929 0.891 0.889 0.895 0.854 0.813 0.801
    RMSE 1.182 1.259 1.177 1.183 1.126 1.053 1.028
    URMSE 1.802 1.756 1.215 1.192 1.168 0.988 0.875
    50% MAE 0.932 0.955 0.923 0.921 0.912 0.875 0.832
    RMSE 1.192 1.263 1.185 1.193 1.236 1.089 1.092
    URMSE 1.820 1.752 1.237 1.214 1.192 1.017 1.002
    20% MAE 0.946 0.957 0.932 0.929 0.918 0.904 0.885
    RMSE 1.205 1.206 1.185 1.123 1.056 1.094 1.085
    URMSE 1.840 1.756 1.255 1.210 1.177 1.036 1.011
    下载: 导出CSV
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  • 收稿日期:  2016-05-13
  • 录用日期:  2016-10-09
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