A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree

被引：0

作者：

Li, Weimin ^{[1
]}

Zhu, Heng ^{[1
]}

Zhou, Xiaokang ^{[2
,3
]}

Shimizu, Shohei ^{[2
,3
]}

Xin, Mingjun ^{[1
,4
]}

Jin, Qun ^{[5
]}

机构：

[1] Shanghai Univ, Sch Comp Engn & Technol, Shanghai, Peoples R China

[2] Shiga Univ, Fac Data Sci, Hikone, Japan

[3] RIKEN Ctr Adv Intelligence Project, Tokyo, Japan

[4] Shanghai Key Lab Comp Software Evaluating & Testi, Shanghai, Peoples R China

[5] Waseda Univ, Fac Human Sci, Tokyo, Japan

来源：

2018 16TH IEEE INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP, 16TH IEEE INT CONF ON PERVAS INTELLIGENCE AND COMP, 4TH IEEE INT CONF ON BIG DATA INTELLIGENCE AND COMP, 3RD IEEE CYBER SCI AND TECHNOL CONGRESS (DASC/PICOM/DATACOM/CYBERSCITECH) | 2018年

基金：

中国国家自然科学基金;

关键词：

personalized recommendation; matrix factorization; collaborative filtering; trust;

D O I：

10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00084

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The rapid development of the Internet and e-commerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.

引用

页码：418 / 422

页数：5

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