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

通信学报 ›› 2018, Vol. 39 ›› Issue (3): 147-158.doi: 10.11959/j.issn.1000-436x.2018044

基于跨平台的在线社交网络用户推荐研究

彭舰¹,王屯屯¹,陈瑜¹,刘唐²,徐文政¹

¹ 四川大学计算机学院，四川成都 610065
² 四川师范大学基础教学学院，四川成都 610068

修回日期:2018-01-10 出版日期:2018-03-01 发布日期:2018-04-02
作者简介:彭舰（1970-），男，四川成都人，博士，四川大学教授，主要研究方向为大数据、传感器计算、移动计算等。|王屯屯（1992-），男，河南安阳人，四川大学硕士生，主要研究方向为数据挖掘、推荐系统、用户行为建模等。|陈瑜（1974-），男，四川成都人，博士，四川大学讲师，主要研究方向为进化计算、机器学习等。|刘唐（1980-），男，四川乐山人，博士，四川师范大学副教授，主要研究方向为无线传感器网络、无线能量传输等。|徐文政（1985-），男，四川成都人，博士，四川大学副研究员，主要研究方向为社交网络、物联网、移动计算。
基金资助:
国家自然科学基金资助项目(U1333113);国家自然科学基金资助项目(61602330);四川省科技支撑计划基金资助项目(2014GZ0111);四川省教育厅科研基金资助项目(18ZA0404)

User recommendation based on cross-platform online social networks

Jian PENG¹,Tuntun WANG¹,Yu CHEN¹,Tang LIU²,Wenzheng XU¹

¹ Computer Science School,Sichuan University,Chengdu 610065,China
² College of Fundamental Education,Sichuan Normal University,Chengdu 610068,China

Revised:2018-01-10 Online:2018-03-01 Published:2018-04-02
Supported by:
The National Natural Science Foundation of China(U1333113);The National Natural Science Foundation of China(61602330);Science and Technology Support Plan Foundation of Sichuan Province(2014GZ0111);The Scientific Research Fund of Sichuan Provincial Education Department(18ZA0404)

摘要/Abstract

摘要：

在社交网络用户推荐研究领域，通过提取用户的行为模式对其进行好友推荐。但是用户的行为是多样性的，在不同的社交平台，用户可能有不同的行为模型。提出跨平台用户推荐模型，同时对用户相关的所有社交网络平台进行建模，最后将用户在所有平台的行为模式进行融合。基于真实的新浪微博数据集和知乎数据集，通过一系列对比实验证明，跨平台用户推荐模型可以更加全面准确地刻画用户行为，更好地进行用户推荐。

关键词: 跨平台, 用户推荐, 在线社交网络, 数据挖掘

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

中图分类号:

TP311

彭舰,王屯屯,陈瑜,刘唐,徐文政. 基于跨平台的在线社交网络用户推荐研究[J]. 通信学报, 2018, 39(3): 147-158.

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.

图/表 9

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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)

基于跨平台的在线社交网络用户推荐研究

User recommendation based on cross-platform online social networks

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参考文献 24

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