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数据分析与知识发现  2017, Vol. 1 Issue (7): 90-99     https://doi.org/10.11925/infotech.2096-3467.2017.07.11
  首届"数据分析与知识发现"学术研讨会专辑(I) 本期目录 | 过刊浏览 | 高级检索 |
基于用户间信任关系改进的协同过滤推荐方法*
薛福亮(), 刘君玲
天津财经大学商学院 天津 300222
Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users
Xue Fuliang(), Liu Junling
Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
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摘要 

目的】利用用户间信任关系改进协同过滤推荐中用户相似性计算精度, 即在目标用户没有相似用户的前提下, 从其信任用户中选择信任值高的作为相似用户, 进而提高相似用户聚类效果, 提高推荐质量, 并有效缓解协同过滤推荐稀疏性和冷启动问题。【方法】筛选信任用户作为相似用户; 根据选择的信任用户和目标用户形成一个项目的评分集, 并对目标用户未评价过的项目进行评分估算(根据信任用户评分进行简单的评分计算); 将用户间的信任关系依据方差大小进行量化, 形成一个调节因子。本文的创新点就在于调节因子的计算, 并将调节因子纳入用户相似性计算, 形成相似性用户聚类簇, 在此基础上在相似用户之间进行交叉推荐。【结果】通过平均绝对误差指标进行实验评价, 结果表明基于信任关系的协同过滤推荐方法相比传统协同过滤, 在推荐精度上更加准确, 并同时有效缓解了冷启动和稀疏性问题。【局限】本文提出的方法仅在具有信任关系的一个算例上进行实验测试, 需在其他数据集和真实应用场景下进一步检验。【结论】用户间信任关系蕴涵非常有价值的信息, 对用户信任关系进行量化, 并纳入用户相似性计算, 在此基础上实施协同过滤推荐, 对缓解冷启动与稀疏性问题具有较好的理论和实践意义。

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作者相关文章
薛福亮
刘君玲
关键词 电子商务推荐用户信任协同过滤冷启动稀疏性    
Abstract

[Objective] This paper tries to improve user similarity calculation in collaborative filtering recommendation with trust relationship among them. Once there is no similar user for members of the target group, we recommend the most trusted ones as the similar users. [Methods] First, we retrieved the trusted users as candidates for the similar users. Second, we combined the trusted and the target users to form a project score set, and evaluated the estimated value of the projects receiving no comment from the target group. Third, we quantified the trust relationship among users to form a regulation factor. Finally, we calculated the adjustment factor and created the similarity cluster of users, and made cross-recommendation among similar users. [Results] The collaborative filtering recommendation method based on trust relationship had better performance than traditional ones. [Limitations] Only examined the new method with one sample dataset with trusted relationship. More research is needed to test the proposed method with other datasets. [Conclusions] The trusted relationship among users contains valuable information, which could be used to calculate user similarity for collaborative filtering recommendation services, and then effectively solves the sparsity and cold start issue.

Key wordsE-commerce Recommendation    User Trust    Collaborative Filtering    Cold Start    Sparsity
收稿日期: 2017-05-26      出版日期: 2017-09-13
ZTFLH:  TP301.6  
基金资助:*本文系教育部人文社会科学一般项目“电子商务环境下顾客购物偏好推荐及企业利润挖掘”(项目编号: 13YJC630195)的研究成果之一
引用本文:   
薛福亮, 刘君玲. 基于用户间信任关系改进的协同过滤推荐方法*[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
Xue Fuliang,Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users. Data Analysis and Knowledge Discovery, 2017, 1(7): 90-99.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.07.11      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I7/90
  引入用户信任关系后的协同推荐核心框架
i1 i2 i3 i4 i5 i6 i7 i8 i9
u1 5
u2 5 4 3 2
u3 4 3 1
u4 3 5 2
u5 4 4 3 3
u6 3 3 5 5
u7 5 4
u8 4 2 1
u9 4 5 5
  用户对项目的评分
u1 u2 u3 u4 u5 u6 u7 u8 u9
u1 1 1
u2 1 1
u3 1 1
u4 1
u5 1 1
u6 1 1
u7
u8
u9
  用户与用户之间的信任关系
u1 u2 u3 u4 u5 u6 u7 u8 u9
d 0 1 1 2 3 4
tu1, uk 1.00 1.00 1.00 0.50 0.33 0.25
  u1和信任网络中的信任用户的信任值
i1 i2 i3 i4 i5 i6 i7 i8 i9
4.33 4 5 3 2.73 1.71
0.29 1.00 1.00 0.29 0.81 0.67
  合并后的u1的评分
u1 u2 u3 u4 u5 u6 u7 u8 u9
S 1.0 0.870 0.992 0.910 0.910 -0.910 1.0 -0.950
S 1.0 0.660 0.995 0.980 0.840 -0.780 0.990 -0.980
S 1.0 0.699 0.955 0.985 0.910 -0.828 0.982 -0.968
  u1和信任用户的相似值
指标 CF CCF ECF
iMAE 0.9985 0.9986 0.9987
F1 0.7994 0.7995 0.7996
  算例上的预测性能表
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