2012/10/19 by Craig Boutilier, Boutilier, Craig, Richard S. Zemel +4
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing #Recommender Systems and Techniques #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1212.2442
Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)
arxiv created 2012/10/19 · openalex publication_date 2012/10/19 · arxiv updated 2012/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.