Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55941
Title: A nonnegative latent factor model for large-scale sparse matrices in Recommender Systems via alternating direction method
Authors: Luo, X
Zhou, M
Li, S 
You, Z
Xia, Y
Zhu, Q
Keywords: Sparse matrices
Alternating direction method
Big data
Collaborative filtering
Recommender system
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2015, v. 27, no. 3, p. 579-592 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and storage complexity, as well as slow convergence rate, which prevents them from industrial usage in context of big data. To address these issues, this paper proposes an alternating direction method (ADM)-based nonnegative latent factor (ANLF) model. The main idea is to implement the ADM-based optimization with regard to each single feature, to obtain high convergence rate as well as low complexity. Both computational and storage costs of ANLF are linear with the size of given data in the target matrix, which ensures high efficiency when dealing with extremely sparse matrices usually seen in CF problems. As demonstrated by the experiments on large, real data sets, ANLF also ensures fast convergence and high prediction accuracy, as well as the maintenance of nonnegativity constraints. Moreover, it is simple and easy to implement for real applications of learning systems.
URI: http://hdl.handle.net/10397/55941
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2015.2415257
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