Improving maximum margin matrix factorization

doi:10.1007/s10994-008-5073-7

Improving maximum margin matrix factorization

2008-07-10

87

OA

AI

M

Markus Weimer *

A

Alexandros Karatzoglou

A

Alex Smola

DOI：10.1007/s10994-008-5073-7

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Abstract

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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

Improving maximum margin matrix factorization

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