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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (12): 95-104    DOI: 10.11925/infotech.2096-3467.2020.0049
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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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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     
Received: 13 January 2020      Published: 25 December 2020
ZTFLH:  TP181  
Corresponding Authors: Xu Jianmin     E-mail: hbuxjm@hbu.edu.cn

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0049     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I12/95

A Framework of Microblog Recommendation Method Combining User’s Interest and Multidimensional Trust
用户 interest1 interestj interestm
user1 p11 p1j p1m
useri pi1 pij pim
usern pn1 pnj pnm
User-Interest Probability Distribution Matrix
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
Abbreviations and Describe of the Methods
影响因素 ηr ηc ηl
ηr 1 2 3
ηc 1/2 1 2
ηl 1/3 1/2 1
Decision Matrix of Interactive Behavior
The Perplexity of LDA Models Under Different Number of Topics
ω1
">
The Max F-Measure of TSR in Different ω1
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
The Performance in Microblog Recommendation Methods Based on Different Trust
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
Decision Matrix of Trust
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
The Performance in Different Microblog Recommendation Methods
实验方法 Top-15 Top-30
CBR 3.273 4.297
TSFR 3.396 4.430
TSFIR 3.413 4.668
DCG in Different Microblog Recommendation Methods at Top-15 and Top-30
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