A Parallelization Algorithm of Singular Value Decomposition

Matrix factorization algorithm is one of the recommendable algorithms. In order to tackle the inefficiencies of the traditional matrix factorization algorithm like the slow training time and the insufficient computing resource for the mass data, a parallelization algorithm of singular value decompos...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1865; no. 4; p. 42004
Main Authors: Duan, Jianfeng, Cun, Xian’e
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.04.2021
Subjects:
Algorithms
Factorization
Parallel processing
Physics
Singular value decomposition
Standardization
Training
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:Matrix factorization algorithm is one of the recommendable algorithms. In order to tackle the inefficiencies of the traditional matrix factorization algorithm like the slow training time and the insufficient computing resource for the mass data, a parallelization algorithm of singular value decomposition (SVD) under the Spark framework is proposed to perform SVD, standardization, and dimensionality reduction for the user-rating matrix, and obtain the user-feature matrix and project-feature matrix. The recommendation model is obtained by determining the prediction rating. MovieLens data show that this algorithm can significantly shorten the training time of the model, improve the running efficiency of the recommendation algorithms for the mass data, and improve the algorithm accuracy.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1865/4/042004