Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Richard S. Zemel
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: zemel@cs.toronto.edu
Benjamin Marlin
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: marlin@cs.toronto.edu
Abstract
Collaborative filtering (CF) allows the preferences of multiple users
to be pooled to make recommendations regarding unseen products.
We consider in this paper the problem of online and interactive CF:
given the current ratings associated with a user, what queries (new
ratings) would most improve the quality of the recommendations made? We
cast this in terms of expected value of information (EVOI); but
the online computational cost of computing optimal queries is prohibitive.
We show how offline prototyping and computation of bounds on EVOI
can be used to dramatically reduce the required online computation.
The framework we develop is general, but we focus on derivations and
empirical study in the specific case of the multiple-cause vector
quantization model.
An earlier version of this paper (which excluded evaluation of naive Bayes models and query prototyping) appeared in Ninth International Workshop on Artificial Intelligence and Statistics (AI-Stats).
To appear, UAI 2003
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