Active Collaborative Filtering

TL;DR

This paper introduces an online, interactive collaborative filtering method that uses offline computations and bounds on expected value of information to efficiently improve recommendation quality.

Contribution

It presents a novel framework combining offline prototyping with bounds on EVOI to enable practical online interactive collaborative filtering.

Findings

Offline bounds significantly reduce online computation

Framework improves recommendation accuracy in experiments

Focus on multiple-cause vector quantization model

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 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.