Informatics, Balkan Conference in (2009)
Sept. 17, 2009 to Sept. 19, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BCI.2009.18
In this paper, we describe and compare threeCollaborative 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 accurate thanthe direct ones. Finally, CA-CF outperformed the SVD-CFand PCA-CF in terms of accuracy for small numbers ofretained dimensions, but SVD-CF displayed the overall highest accuracy.
Collaborative Filtering, Singular Value Decomposition, Correspondence Analysis, Principal Component Analysis
M. Vozalis, A. Markos and K. Margaritis, "On the Performance of SVD-Based Algorithms for Collaborative Filtering," Informatics, Balkan Conference in(BCI), Thessaloniki, Greece, 2009, pp. 245-250.