A Semantic Model for Social Recommender Systems

被引：0
|
作者：
Kim, Heung-Nam [1 ,2 ]
Roczniak, Andrew [1 ]
Levy, Pierre [1 ]
El-Saddik, Abdulmotaleb [2 ]
机构：
[1] Univ Ottawa, Collect Intelligence Lab, Ottawa, ON K1N 6N5, Canada
[2] Univ Ottawa, Multimedia Commun Res Lab, Ottawa, ON K1N 6N5, Canada
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2010年 / 6085卷
关键词：
Social Recommender System; IEML Semantic Model;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Social recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of items suited to their needs. To provide proper recommendations to users, social recommender systems require accurate models of characteristics, interests and needs for each user. In this paper, we introduce a new model capturing semantics of user-generated tags and propose a social recommender system that is incorporated with the semantics of the tags. Our approach first determines semantically similar items by utilizing semantic-oriented tags and secondly discovers semantically relevant items that are more likely to fit users' needs.
引用
收藏
页码：328 / +
页数：2
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