Paper Info
Title
On the Performance of SVD-Based Algorithms for Collaborative Filtering
Abstract
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.
Year
DOI
Venue
2009
10.1109/BCI.2009.18
BCI
Keywords
Field
DocType
approximation algorithms,factor model,collaboration,principal component analysis,prediction algorithms,groupware,singular value,correspondence analysis,singular value decomposition,low rank approximation,algorithm design and analysis,collaborative filtering,matrix decomposition,principal component
Approximation algorithm,Singular value decomposition,Collaborative filtering,Algorithm design,Matrix (mathematics),Computer science,Matrix decomposition,Algorithm,Low-rank approximation,Principal component analysis
Conference
Citations 
PageRank 
References 
1
0.35
12
Authors
3
Name
Order
Citations
PageRank
Manolis G. Vozalis1605.53
Angelos I. Markos2555.72
Konstantinos G. Margaritis330345.46