Active Collaborative Filtering

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 multiplecause vector quantization model.

• Type: Conference
• Year: 2003
• Where: UAI
• Authors: Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin
• Keywords: Multiplecause Vector Quantization, Online Computational Cost, Required Online Computation, UAI 2003, UAI 2008