Active Collaborative Filtering
This addresses the challenge of efficient query selection for users in recommendation systems, but it is incremental as it builds on existing EVOI methods with computational optimizations.
The paper tackles the problem of online and interactive collaborative filtering by identifying which new user ratings would most improve recommendation quality, using expected value of information (EVOI). It reduces prohibitive online computation through offline prototyping and bounds on EVOI, with empirical study in the multiple-cause vector quantization model.
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.