Folksonomy-based personalized search by hybrid user profiles in multiple levels

被引：27

作者：

Du, Qing ^{[1
]}

Xie, Haoran ^{[2
]}

Cai, Yi ^{[1
]}

Leung, Ho-fung ^{[3
]}

Li, Qing ^{[4
]}

Min, Huaqing ^{[1
]}

Wang, Fu Lee ^{[5
]}

机构：

[1] S China Univ Technol, Sch Software Engn, Guangzhou, Guangdong, Peoples R China

[2] Hong Kong Inst Educ, Dept Math & Informat Technol, Hong Kong, Hong Kong, Peoples R China

[3] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China

[4] City Univ Hong Kong, Dept Comp Sci, 83 Tat Chee Ave, Kowloon, Hong Kong, Peoples R China

[5] Caritas Inst Higher Educ, Hong Kong, Hong Kong, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 204卷

基金：

中国国家自然科学基金;

关键词：

Folksonomy; Social tagging; Web; 2.0; User profiling; Personalized search; NEWS IMPACT; COMMUNITY;

D O I：

10.1016/j.neucom.2015.10.135

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently, some systems have allowed users to rate and annotate resources, e.g., MovieLens, and we consider that it provides a way to identify favorite and non-favorite tags of a user by integrating his or her rating and tags. In this paper, we review and elaborate on the limitations of the current research on user profiling for personalized search in collaborative tagging systems. We then propose a new multi-level user profiling model by integrating tags and ratings to achieve personalized search, which can reflect not only a user's likes but also a his or her dislikes. To the best of our knowledge, this is the,first effort to integrate ratings and tags to model multi-level user profiles for personalized search. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：142 / 152

页数：11

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