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数据分析与知识发现  2020, Vol. 4 Issue (12): 95-104     https://doi.org/10.11925/infotech.2096-3467.2020.0049
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
融合用户兴趣和多维信任度的微博推荐*
韩康康1,徐建民1(),张彬2
1河北大学网络空间安全与计算机学院 保定 071002
2河北大学管理学院 保定 071002
Recommending Microblogs with User’s Interests and Multidimensional Trust
Han Kangkang1,Xu Jianmin1(),Zhang Bin2
1School of Cyberspace Security and Computer, Hebei University, Baoding 071002, China
2School of Management, Hebei University, Baoding 071002, China
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摘要 

【目的】 利用微博发布者和目标用户的多维信任关系对传统的微博推荐方法进行改进,以获得更好的推荐效果。【方法】 通过将微博发布者和目标用户的相似信任度、熟悉信任度和影响力信任度线性调和,得到二者间的综合信任度,将其作为调整因子对基于内容的微博推荐方法进行改进。【结果】 在真实数据上的实验结果表明,与传统的微博推荐方法相比,改进方法在F值和DCG值上均有一定程度提高。【局限】 仅考虑相邻用户间的直接关系,未考虑不相邻用户间的间接关系。【结论】 利用多维信任度改进传统微博推荐方法,可以提高推荐效果。

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韩康康
徐建民
张彬
关键词 微博推荐相似信任度熟悉信任度影响力信任度    
Abstract

[Objective] This paper tries to improve microblog recommendation method with the trust relationship between microblog profiles and target users, aiming to improve the recommendation results. [Methods] First, the comprehensive trust between microblog users and target users is calculated by using the linear harmonic function of similarity, familiarity and influence. Then, the comprehensive trust degree is used as the adjustment factor to improve the content-based recommendation method. [Results] The F-Measure and DCG-Measure of the method was higher than those of the traditional ones. [Limitations] This method did not examine the indirect relationship among the non-adjacent users. [Conclusions] The proposed method could more effectively recommend microblogs.

Key wordsMicroblog Recommendation    Similarity Trust    Familiarity Trust    Influence Trust
收稿日期: 2020-01-13      出版日期: 2020-12-25
ZTFLH:  TP181  
基金资助:*国家社会科学基金后期资助项目“基于术语关系的贝叶斯网络检索模型扩展”(17FTQ002);河北省自然科学基金项目“基于贝叶斯网络的话题识别与追踪方法研究”(F2015201142)
通讯作者: 徐建民     E-mail: hbuxjm@hbu.edu.cn
引用本文:   
韩康康,徐建民,张彬. 融合用户兴趣和多维信任度的微博推荐*[J]. 数据分析与知识发现, 2020, 4(12): 95-104.
Han Kangkang,Xu Jianmin,Zhang Bin. Recommending Microblogs with User’s Interests and Multidimensional Trust. Data Analysis and Knowledge Discovery, 2020, 4(12): 95-104.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0049      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I12/95
Fig.1  融合用户兴趣和多维信任度的微博推荐方法框架
用户 interest1 interestj interestm
user1 p11 p1j p1m
useri pi1 pij pim
usern pn1 pnj pnm
Table 1  用户-兴趣概率分布矩阵
方法简写 方法解释
TSR 基于相似信任度的微博推荐方法
TFR 基于熟悉信任度的微博推荐方法
TIR 基于影响力信任度的微博推荐方法
TSFIR 融合用户兴趣和多维信任度的微博推荐方法
BCR 传统基于内容的微博推荐方法
TSFR[10] 基于相似度和信任度融合的微博推荐方法
Table 2  方法简写及其描述
影响因素 ηr ηc ηl
ηr 1 2 3
ηc 1/2 1 2
ηl 1/3 1/2 1
Table 3  交互行为的判定矩阵
Fig.2  不同主题个数下LDA模型的困惑度
Fig.3  不同ω1下TSR的最大F值
实验方法 准确率 召回率 F值
TSR 0.742 0.761 0.751
TFR 0.679 0.652 0.665
TIR 0.711 0.721 0.716
Table 4  基于不同信任度的微博推荐方法的性能比较
影响因素 Trust_Sim Trust_Fam Trust_Inf
Trust_Sim 1 5 3
Trust_Fam 1/5 1 1/3
Trust_Inf 1/3 3 1
Table 5  信任度的判定矩阵
实验方法 准确率 召回率 F值
CBR 0.728 0.767 0.747
TSFR 0.814 0.832 0.823
TSFIR 0.833 0.829 0.831
Table 6  不同微博推荐方法的性能比较
实验方法 Top-15 Top-30
CBR 3.273 4.297
TSFR 3.396 4.430
TSFIR 3.413 4.668
Table 7  不同微博推荐方法在Top-15和Top-30的DCG值比较
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