Leveraging Aggregate Ratings for Better Recommendations

7 Pages Posted: 9 Oct 2008

See all articles by Akhmed Umyarov

Akhmed Umyarov

New York University (NYU) - Leonard N. Stern School of Business

Alexander Tuzhilin

New York University (NYU) - Leonard N. Stern School of Business; New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: September 2007

Abstract

The paper presents a method that uses aggregate ratingsprovided by various segments of users for various categoriesof items to derive better estimations of unknown individualratings. This is achieved by converting the aggregate ratingsinto constraints on the parameters of a rating estimationmodel presented in the paper. The paper also demonstratestheoretically that these additional constraints reduce ratingestimation errors resulting in better rating predictions.

Keywords: Recommender systems, Hierarchical Bayesian models, predictive models, aggregate ratings, OLAP

Suggested Citation

Umyarov, Akhmed and Tuzhilin, Alexander, Leveraging Aggregate Ratings for Better Recommendations (September 2007). NYU Working Paper No. CEDER-07-03. Available at SSRN: https://ssrn.com/abstract=1281344

Akhmed Umyarov (Contact Author)

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Alexander Tuzhilin

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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