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Machine Learning

, Volume 72, Issue 3, pp 263–276 | Cite as

Improving maximum margin matrix factorization

  • Markus Weimer
  • Alexandros Karatzoglou
  • Alex Smola
Article

Abstract

Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses. In this paper we discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph. We show equivalence between graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.

Keywords

Collaborative filtering Structured estimation Recommender systems 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Markus Weimer
    • 1
  • Alexandros Karatzoglou
    • 2
  • Alex Smola
    • 3
  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.INSA de Rouen, LITISRouenFrance
  3. 3.NICTACanberraAustralia

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