Social media filtering based on collaborative tagging in semantic space

被引：10
|
作者：
Kim, Heung-Nam [1 ,2 ]
Roczniak, Andrew
Levy, Pierre [1 ,3 ]
El Saddik, Abdulmotaleb [2 ]
机构：
[1] Univ Ottawa, Collect Intelligence Lab, Canada Res Chair Collect Intelligence, Ottawa, ON, Canada
[2] Univ Ottawa, Multimedia Commun Res Lab, Ottawa, ON, Canada
[3] Univ Ottawa, Dept Commun, Ottawa, ON, Canada
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2012年 / 56卷 / 01期
关键词：
Social recommender system; Semantic collaborative filtering; Social tagging; Folksonomy; Semantic tagging; IEML;
D O I：
10.1007/s11042-010-0557-4
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose a semantic collaborative filtering method to enhance recommendation quality derived from user-generated tags. Social tagging is employed as an approach in order to grasp and filter users' preferences for items. In addition, we explore several advantages of semantic tagging for ambiguity, synonymy, and semantic interoperability, which are notable challenges in information filtering. The proposed approach first determines semantically similar users using social tagging and subsequently discovers semantically relevant items for each user. Experimental results show that our method offers significant advantages both in terms of improving the recommendation quality and in dealing with ambiguity, synonymy, and interoperability issues.
引用
收藏
页码：63 / 89
页数：27
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