Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
Adomavicius, G.
Tuzhilin, A.
Carlson Sch. of Manage., Minnesota Univ., Minneapolis, MN, USA;
This paper appears in: Knowledge and Data Engineering, IEEE Transactions on
Publication Date: June 2005
Volume: 17,
Issue: 6
On page(s): 734- 749
ISSN: 1041-4347
INSPEC Accession Number: 8461697
Digital Object Identifier: 10.1109/TKDE.2005.99
Current Version Published: 2005-04-25
Abstract
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 multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
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