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Experimental Evaluation on Matrix Decomposition Methods

  • Panagiotis SymeonidisEmail author
  • Andreas Zioupos
Chapter
  • 1.4k Downloads
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In this chapter, we study the performance of described SVD and UV decomposition algorithms, against an improved version of the original item based CF algorithm combined with SVD. For the UV decomposition method, we will present the appropriate tuning of parameters of its objective function to have an idea of how we can get optimized values of its parameters. We will also answer the question if these values are generally accepted or they should be different for each data set. The metrics we will use are root-mean-square error (RMSE), precision, and recall. The size of a training set is fixed at 75 %, and we perform a fourfold cross-validation.

Keywords

Experiments SVD decomposition UV decomposition 

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBozen-BolzanoItaly
  2. 2.ThessalonikiGreece

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