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AUSAI
2008
Springer

Additive Regression Applied to a Large-Scale Collaborative Filtering Problem

11 years 1 months ago
Additive Regression Applied to a Large-Scale Collaborative Filtering Problem
Abstract. The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The demanding nature of the Netflix data has lead to some interesting and ingenious modifications to standard learning methods in the name of efficiency and speed. There are three basic methods that have been applied in most approaches to the Netflix problem so far: stand-alone neighborhood-based methods, latent factor models based on singular-value decomposition, and ensembles consisting of variations of these techniques. In this paper we investigate the application of forward stage-wise additive modeling to the Netflix problem, using two regression schemes as base learners: ensembles of weighted simple linear regressors and k-means clustering--the latter being interpreted as a tool for multivariate regression in this context. Experimental results show that our m...
Eibe Frank, Mark Hall
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where AUSAI
Authors Eibe Frank, Mark Hall
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