Collaborative Filtering via Ensembles of Matrix Factorizations

Wu, M

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Collaborative Filtering via Ensembles of Matrix Factorizations

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Wu, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.cs.uic.edu/~liub/KDD-cup-2007/proceedings.html
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KDDW-2007-Wu.pdf
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Citation

Wu, M. (2007). Collaborative Filtering via Ensembles of Matrix Factorizations. In KDD Cup and Workshop 2007 (pp. 43-47).

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CC3D-E

Abstract

We present a Matrix Factorization(MF) based approach for the Netflix Prize competition. Currently MF based algorithms are popular and have proved successful for collaborative filtering tasks. For the Netflix Prize competition, we adopt three different types of MF algorithms: regularized MF, maximum margin MF and non-negative MF. Furthermore, for each MF algorithm, instead of selecting the optimal parameters, we combine the results obtained with several parameters. With this method, we achieve a performance that is more than 6 better than the Netflix's own system.