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dc.contributor.authorFrank, Eibe
dc.contributor.authorHall, Mark A.
dc.coverage.spatialConference held at Auckland, New Zealanden_NZ
dc.identifier.citationFrank, E. & Hall, M. (2008).Additive regression applied to a large-scale collaborative filtering problem. In W. Wobcke and M. Zhange(Eds.), Proceedings of 21st Australasian Joint Conference on Artificial Intelligence Auckland, New Zealand, December 1-5, 2008(pp. 435-446). Berlin: Springer.en
dc.description.abstractThe 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 multi-variate regression in this context. Experimental results show that our methods produce competitive results.en
dc.sourceAI 2008en_NZ
dc.subjectcomputer scienceen
dc.subjectlogistic regressionen
dc.subjectMachine learning
dc.titleAdditive Regression Applied to a Large-Scale Collaborative Filtering Problemen
dc.typeConference Contributionen
dc.relation.isPartOfProc Twenty-first Australian Joint Conference on Artificial Intelligenceen_NZ
pubs.volumeLecture Notes in Artificial Intelligence 5360en_NZ

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