基于跨平台的在线社交网络用户推荐研究

doi:10.11959/j.issn.1000-436x.2018044

Abstract

Abstract:

In the field of online social networks on user recommendation,researchers extract users’ behaviors as much as possible to model the users.However,users may have different likes and dislikes in different social networks.To tackle this problem,a cross-platform user recommendation model was proposed,users would be modeled all-sided.In this study,the Sina micro blog and the Zhihu were investigated in the proposed model,the experimental results show that the proposed model is competitive.Based on the proposed model and the experimental results,it can be known that modeling users in cross-platform online social networks can describe the user more comprehensively and leads to a better recommendation.

Key words: cross-platform, user recommendation, online social networks, data mining

CLC Number:

TP311

Jian PENG,Tuntun WANG,Yu CHEN,Tang LIU,Wenzheng XU. User recommendation based on cross-platform online social networks[J]. Journal on Communications, 2018, 39(3): 147-158.

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References 24

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符号	描述
D	所有用户的全部文档集合
T	所有文档的主题集合
d	一个文档
t	单个文档的主题单词
N	单个文档的主题数量
P_{doc_topic}	单个文档的概率主题分布
Sim	2个用户间的相似度
e	社交网络平台的数量
R	单个平台的主题数量
VOC	所有文档组成的单词集合
θ	所有平台的权值向量
H(x)	预测函数
J(x)	损失函数
Precision	模型的准确率
Recall	模型的召回率
F₁	模型的F₁值
MAP	模型的平均准确率
D	2个用户间的距离
cos	2个向量间的相似性
L	学习步长

L	θ_init	θ_final
0.01	(0,0)	(?0.018 571 521,?0.011 475 758)
0.01	(1,1)	(?0.018 571 521,?0.011 475 758)
0.01	(0,1)	(?0.018 571 521,?0.011 475 758)
0.01	(1,0)	(?0.018 571 521,?0.011 475 758)
0.1	(0,0)	(?0.129 991 725,?0.108 686 021)
0.1	(1,1)	(?0.129 991 725,?0.108 686 021)
0.1	(1,0)	(0.060 068 123,0.077 906 093)
0.1	(0,1)	(0.060 068 123,0.077 906 093)

User recommendation based on cross-platform online social networks

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