New Recommendation Techniques for Multicriteria Rating Systems

Authors:
Gediminas Adomavicius;YoungOk Kwon
Affiliations:
University of Minnesota;University of Minnesota
Venue:
IEEE Intelligent Systems
Year:
2007

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Abstract

Traditional single-rating recommender systems have been successful in a number of personalization applications, but the research area of multicriteria recommender systems has been largely untouched. Taking full advantage of multicriteria ratings in various applications requires new recommendation techniques. The authors propose two new approaches—a similarity-based approach and an aggregation-function-based approach—to incorporating and leveraging multicriteria rating information in recommender systems. They discuss multiple variations of each proposed approach and report empirical analysis results from a real-world dataset. Experimental results show that recommender systems can leverage multicriteria ratings and improve recommendation accuracy, as compared to traditional single-rating recommendation techniques.