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Improving Maximum Margin Matrix Factorization

Abstract : 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. Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.
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Contributor : Alexandros Karatzoglou <>
Submitted on : Tuesday, May 11, 2010 - 1:17:41 PM
Last modification on : Thursday, June 14, 2018 - 11:46:01 AM
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  • HAL Id : hal-00482747, version 1


Markus Weimer, Alexandros Karatzoglou, Alex Smola. Improving Maximum Margin Matrix Factorization. Machine Learning, Springer Verlag, 2008, 72 (3), pp.263-276. ⟨hal-00482747⟩



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