[StreamGPU] Singular Value Decomposition for Collaborative Filtering on a GPU

協調フィルタリング用 特異値分解の GPU 用による高速化
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Singular Value Decomposition for Collaborative Filtering on a GPU, 2010

A collaborative ltering predicts customers' unknown preferences from known preferences. In a computation of the collaborative ltering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an e ffective algorithm to compute SVD toward a solution of an open competition called "NetixPrize". The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be effi cient by an experiment.


http://iopscience.iop.org/1757-899X/10/1/012017/pdf/1757-899X_10_1_012017.pdf

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Collaborative Filtering 関連の参考論文

A Survey of Collaborative Filtering Techniques, 2009
http://www.hindawi.com/journals/aai/2009/421425.html

Yunhong Zhou, Large-scale Parallel Collaborative Filtering forthe Netflix Prize
http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf