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      • Computing and Mathematical Sciences
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      Additive Regression Applied to a Large-Scale Collaborative Filtering Problem

      Frank, Eibe; Hall, Mark A.
      DOI
       10.1007/978-3-540-89378-3_44
      Link
       www.springerlink.com
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      Citation
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      Frank, 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.
      Permanent Research Commons link: https://hdl.handle.net/10289/1771
      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 multi-variate regression in this context. Experimental results show that our methods produce competitive results.
      Date
      2008
      Type
      Conference Contribution
      Publisher
      Springer
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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