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WECWIS
2005
IEEE
137views ECommerce» more  WECWIS 2005»
13 years 10 months ago
Using Singular Value Decomposition Approximation for Collaborative Filtering
Singular Value Decomposition (SVD), together with the Expectation-Maximization (EM) procedure, can be used to find a low-dimension model that maximizes the loglikelihood of obser...
Sheng Zhang, Weihong Wang, James Ford, Fillia Make...
BCI
2009
IEEE
13 years 11 months ago
On the Performance of SVD-Based Algorithms for Collaborative Filtering
—In this paper, we describe and compare three Collaborative Filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implemen...
Manolis G. Vozalis, Angelos I. Markos, Konstantino...
PCI
2001
Springer
13 years 9 months ago
An Experimental Evaluation of a Monte-Carlo Algorithm for Singular Value Decomposition
We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values of a matrix A, is not only of theoretical interest but also a fast, v...
Petros Drineas, Eleni Drinea, Patrick S. Huggins
IJCSA
2006
197views more  IJCSA 2006»
13 years 4 months ago
Applying SVD on Generalized Item-based Filtering
In this paper we examine the use of a matrix factorization technique called Singular Value Decomposition (SVD) along with demographic information
Manolis G. Vozalis, Konstantinos G. Margaritis
JSC
2008
162views more  JSC 2008»
13 years 4 months ago
Approximate factorization of multivariate polynomials using singular value decomposition
We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficie...
Erich Kaltofen, John P. May, Zhengfeng Yang, Lihon...