ECML PKDD 2008
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
15 - 19 September 2008, Antwerp, Belgium
Paper abstractImproving Maximum Margin Matrix FactorizationMarkus Weimer - Technische Universitat Darmstadt, GermanyAlexandros Karatzoglou - INSA de Rouen, France Alex Smola - NICTA, Australia Session: Matrix Factorization Springer Link: http://dx.doi.org/10.1007/978-3-540-87479-9_12 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 showimproved performance over the original MMMF formulation. |