Tag-Aware Recommender Systems:A State-of-the-Art Survey

被引：32

作者：

张子柯 ^{[1
,2
,3
]}

周涛 ^{[2
]}

张翼成 ^{[1
,2
,3
]}

机构：

[1] Institute of Information Economy,Hangzhou Normal University

[2] Web Sciences Center,University of Electronic Science and Technology

[3] Department of Physics,University of Fribourg,Chemin du Mus'ee 1700 Fribourg,Switzerland

来源：

JournalofComputerScience&Technology | 2011年 / 26卷 / 05期

基金：

中国国家自然科学基金;

关键词：

social tagging systems; tag-aware recommendation; network-based/tensor-based/topic-based methods;

D O I：

暂无

中图分类号：

TP391.3 [检索机];

学科分类号：

081203 ; 0835 ;

摘要：

In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been addressed to unify tagging information to reveal user behaviors and preferences,extract the latent semantic relations among items,make recommendations,and so on.Specifically,this article summarizes recent progress about tag-aware recommender systems,emphasizing on the contributions from three mainstream perspectives and approaches:network-based methods,tensor-based methods,and the topic-based methods.Finally,we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.

引用

页码：767 / 777

页数：11