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2009
IEEE

On the Performance of SVD-Based Algorithms for Collaborative Filtering

11 years 8 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 implementations are based on three standard techniques for fitting a factor model to the data: Standard Singular Value Decomposition (sSVD), Principal Component Analysis (PCA) and Correspondence Analysis (CA). CA and PCA can be described as SVDs of appropriately transformed matrices, which is a key concept in this study. For each algorithm we implement two similar CF versions. The first one involves a direct rating prediction scheme based on the reduced user-item ratings matrix, while the second incorporates an additional neighborhood formation step. Next, we examine the impact of the aforementioned approaches on the quality of the generated predictions through a series of experiments. The experimental results showed that the approaches including the neighborhood formation step in most cases appear to be less accur...
Manolis G. Vozalis, Angelos I. Markos, Konstantino
Added 18 May 2010
Updated 18 May 2010
Type Conference
Year 2009
Where BCI
Authors Manolis G. Vozalis, Angelos I. Markos, Konstantinos G. Margaritis
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