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Empirical Study of Matrix Factorization Methods for Collaborative Filtering

  • Evgeny Kharitonov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

Matrix factorization methods have proved to be very efficient in collaborative filtering tasks. Regularized empirical risk minimization with squared error loss function and L 2-regularization and optimization performed via stochastic gradient descent (SGD) is one of the most widely used approaches.

The aim of the paper is to experimentally compare some modifications of this approach. Namely, we compare Huber’s, smooth ε-insensitive and squared error loss functions. Moreover, we investigate a possibility to improve the results by applying a more sophisticated optimization technique — stochastic meta-descent (SMD) instead of SGD.

Keywords

collaborative filtering matrix factorization loss functions 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Evgeny Kharitonov
    • 1
  1. 1.Moscow Institute of Physics and TechnologyRussia

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