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Improving Maximum Margin Matrix Factorization <b> (best machine learning paper award)</b>

Weimer, Markus and Karatzoglou, Alexandros and Smola, Alex
Daelemans, Walter and Goethals, Bart and Morik, Katharina (eds.) :

Improving Maximum Margin Matrix Factorization <b> (best machine learning paper award)</b>.
In: LNAI , 5211 . Springer
[Conference or Workshop Item] , (2008)

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: Conference or Workshop Item
Erschienen: 2008
Editors: Daelemans, Walter and Goethals, Bart and Morik, Katharina
Creators: Weimer, Markus and Karatzoglou, Alexandros and Smola, Alex
Title: Improving Maximum Margin Matrix Factorization <b> (best machine learning paper award)</b>
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.

Title of Book: Machine Learning and Knowledge Discovery in Databases
Series Name: LNAI
Volume: 5211
Publisher: Springer
Divisions: Department of Computer Science > Telecooperation
Department of Computer Science
Date Deposited: 31 Dec 2016 12:59
Identification Number: TUD-CS-2008-1210
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