New Recommendation Techniques for Multicriteria Rating Systems

TLDR

Personalization technologies and recommender systems, now ubiquitous across online shopping and other platforms, mitigate information overload but rely on single‑rating approaches; leveraging multicriteria ratings promises higher accuracy, necessitating new recommendation techniques. The study proposes several novel techniques to extend recommendation technologies by incorporating and leveraging multicriteria rating information. The authors develop multiple new algorithms that integrate multicriteria rating data into recommendation models.

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.