期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 27, 期 3, 页码 579-592出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2415257
关键词
Alternating direction method; big data; collaborative filtering; recommender system; sparse matrices
类别
资金
- Hong Kong, Macao and Taiwan Science and Technology Cooperation Program of China [2013DFM10100]
- NSFC [61202347, 61272194, 61472051, 61401385, 61373086]
- U.S. NSF [CMMI-1162482]
- Young Scientist Foundation of Chongqing [cstc2014kjrc-qnrc40005, cstc2013kjrc-qnrc0079]
- Post-Doctoral Science Funded Project of Chongqing [Xm2014043]
- China Post-Doctoral Science Foundation [2014M562284]
- Fundamental Research Funds for the Central Universities [106112015CDJXY180005, 106112014CDJZR185503]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120191120030]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1162482] Funding Source: National Science Foundation
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.
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