Gediminas Adomavicius

[Full Paper in pdf]

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 classical User and Item dimensions and also OLAP-type aggregation and filtering capabilities. The paper also presents a recommendation algebra, shows how REQUEST recommendations can be mapped into this algebra, and analyzes the expressive power of the query language and the algebra. Finally, the paper shows how users can customize their recommendations using REQUEST queries through a series of examples.

KEYWORDS:Personalization, recommender systems, recommendation query language, recommendation algebra.