Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

被引:5251
|
作者
Adomavicius, G
Tuzhilin, A
机构
[1] Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA
[2] Stern Sch Business, New York, NY 10012 USA
关键词
recommender systems; collaborative filtering; rating estimation methods; extensions to recommender systems;
D O I
10.1109/TKDE.2005.99
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
引用
收藏
页码:734 / 749
页数:16
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