New Recommendation Techniques for Multicriteria Rating Systems

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.

[1]  Roman B. Statnikov,et al.  Multicriteria Optimization and Engineering , 1995 .

[2]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[3]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[4]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[5]  Martin Bichler,et al.  An experimental analysis of multi-attribute auctions , 2000, Decis. Support Syst..

[6]  E GreenPaul,et al.  Thirty Years of Conjoint Analysis , 2001 .

[7]  P. Green,et al.  Thirty Years of Conjoint Analysis: Reflections and Prospects , 2001 .

[8]  Francesco Ricci,et al.  Case Base Querying for Travel Planning Recommendation , 2001, J. Inf. Technol. Tour..

[9]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[10]  Chih-Hung Liu,et al.  Intelligent agent-based systems for personalized recommendations in Internet commerce , 2002, Expert Syst. Appl..

[11]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[12]  J. Ben Schafer,et al.  DynamicLens: A Dynamic User-Interface for a Meta-Recommendation System , 2005 .

[13]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[14]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.