A Personalized News Recommendation System Based on Tag Dependency Graph

被引：2

作者：

Ai, Pengqiang ^{[1
]}

Xiao, Yingyuan ^{[1
,2
]}

Zhu, Ke ^{[1
]}

Wang, Hongya ^{[3
]}

Hsu, Ching-Hsien ^{[1
,4
]}

机构：

[1] Tianjin Univ Technol, Tianjin 300384, Peoples R China

[2] Tianjin Key Lab Intelligence Comp & Novel Softwar, Tianjin 300384, Peoples R China

[3] Donghua Univ, Shanghai 201620, Peoples R China

[4] Chung Hua Univ, Hsinchu 30012, Taiwan

来源：

WEB-AGE INFORMATION MANAGEMENT (WAIM 2015) | 2015年 / 9098卷

关键词：

D O I：

10.1007/978-3-319-21042-1_68

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The tags of news articles give readers the most important and relevant information regarding the news articles, which are more useful than a simple bag of keywords extracted from news articles. Moreover, latent dependency among tags can be used to assign tags with different weight. Traditional content-based recommendation engines have largely ignored the latent dependency among tags. To solve this problem, we implemented a prototype system called PRST, which is presented in this paper. PRST builds a tag dependency graph to capture the latent dependency among tags. The demonstration shows that PRST makes news recommendation more effectively.

引用

页码：584 / 586

页数：3

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