  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 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.
