DocumentCode
892513
Title
New Recommendation Techniques for Multicriteria Rating Systems
Author
Adomavicius, Gediminas ; Kwon, Youngok
Author_Institution
Minnesota Univ., Minneapolis, MN
Volume
22
Issue
3
fYear
2007
Firstpage
48
Lastpage
55
Abstract
Personalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. Two new recommendation techniques leverage multicriteria ratings and improve recommendation accuracy as compared with single-rating recommendation approaches. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information.
Keywords
information filtering; retail data processing; multicriteria rating systems; online shopping sites; personalization technologies; recommender systems; Batteries; Books; Circuits; Collaboration; Consumer electronics; Displays; Feedback; Motion pictures; Predictive models; Recommender systems; collaborative filtering; multicriteria ratings; personalization; rating estimation; recommender systems;
fLanguage
English
Journal_Title
Intelligent Systems, IEEE
Publisher
ieee
ISSN
1541-1672
Type
jour
DOI
10.1109/MIS.2007.58
Filename
4216980
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