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Improving Maximum Margin Matrix Margin Factorization

Weimer, Markus and Karatzoglou, Alexandros and Smola, Alex (2008):
Improving Maximum Margin Matrix Margin Factorization.
In: Machine Learning, 72 (3), pp. 263-276, [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.

Item Type: Article
Erschienen: 2008
Creators: Weimer, Markus and Karatzoglou, Alexandros and Smola, Alex
Title: Improving Maximum Margin Matrix Margin Factorization
Language: German
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.

Journal or Publication Title: Machine Learning
Volume: 72
Number: 3
Divisions: 20 Department of Computer Science > Telecooperation
20 Department of Computer Science
Date Deposited: 31 Dec 2016 12:59
Identification Number: TUD-CS-2008-1216
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