Experimental Evaluation on Matrix Decomposition Methods

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


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.


Experiments SVD decomposition UV decomposition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Drineas, P., Kannan, R., Mahoney, M.W.: Fast monte carlo algorithms for matrices III: computing a compressed approximate matrix decomposition. SIAM J. Comput. 36(1), 184–206 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Herlocker, J., Konstan, J., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retrieval 5(4), 287–310 (2002)CrossRefGoogle Scholar
  3. 3.
    Mahoney, M.W., Drineas, P.: Cur matrix decompositions for improved data analysis. Proc. Natl. Acad. Sci. 106(3), 697–702 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Mahoney, M.W., Maggioni, M., Drineas, P.: Tensor-cur decompositions for tensor-based data. SIAM J. Matrix Anal. Appl. 30(3), 957–987 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    McLauglin, R., Herlocher, J.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of ACM SIGIR Conference, pp. 329–336 (2004)Google Scholar
  6. 6.
    Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y.: Collaborative recommender systems: combining effectiveness and efficiency. Expert Syst. Appl. 34(4), 2995–3013 (2008)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

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

Personalised recommendations