REQUEST: A Query Language for Customizing Recommendations

19 Pages Posted: 9 Oct 2008 Last revised: 30 Aug 2014

See all articles by Gediminas Adomavicius

Gediminas Adomavicius

University of Minnesota - Twin Cities - Carlson School of Management

Alexander Tuzhilin

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

Rong Zheng

Hong Kong University of Science and Technology - Business School - Department of Information Systems, Business Statistics and Operations Management

Date Written: 2011

Abstract

Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years. However, the types of recommendations available to the users in these recommender systems are typically determined by the vendor and therefore are not flexible. In this paper, we address this problem by presenting the recommendation query language REQUEST that allows users to customize recommendations by formulating them in the ways satisfying personalized needs of the users. REQUEST is based on the multidimensional model of recommender systems that supports additional contextual dimensions besides traditional User and Item dimensions and also OLAP-type aggregation and filtering capabilities. This paper also presents the recommendation algebra RA, shows how REQUEST recommendations can be mapped into this algebra, and analyzes the expressive power of the query language and the algebra. This paper also shows how users can customize their recommendations using REQUEST queries through a series of examples.

Keywords: personalization,recommender systems, recommendation query language, multidimensional recommendations, contextual recommendations, recommendation algebra

Suggested Citation

Adomavicius, Gediminas and Tuzhilin, Alexander and Zheng, Rong, REQUEST: A Query Language for Customizing Recommendations (2011). REQUEST: A Query Language for Customizing Recommendations. Information System Research. vol. 22 no. 1 99-117, (2011). Available at SSRN: https://ssrn.com/abstract=1281306

Gediminas Adomavicius (Contact Author)

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
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

Rong Zheng

Hong Kong University of Science and Technology - Business School - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

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