• 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