Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/631796
Title: A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method
Authors: Xin Luo;MengChu Zhou;Shuai Li;Zhuhong You;Yunni Xia;Qingsheng Zhu
subject: recommender system|Alternating direction method|sparse matrices.|collaborative filtering|big data
Year: 2016
Publisher: IEEE
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://localhost/handle/Hannan/165622
http://localhost/handle/Hannan/631796
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
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Title: A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method
Authors: Xin Luo;MengChu Zhou;Shuai Li;Zhuhong You;Yunni Xia;Qingsheng Zhu
subject: recommender system|Alternating direction method|sparse matrices.|collaborative filtering|big data
Year: 2016
Publisher: IEEE
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://localhost/handle/Hannan/165622
http://localhost/handle/Hannan/631796
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7112169.pdf3.84 MBAdobe PDFThumbnail
Preview File
Title: A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method
Authors: Xin Luo;MengChu Zhou;Shuai Li;Zhuhong You;Yunni Xia;Qingsheng Zhu
subject: recommender system|Alternating direction method|sparse matrices.|collaborative filtering|big data
Year: 2016
Publisher: IEEE
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://localhost/handle/Hannan/165622
http://localhost/handle/Hannan/631796
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7112169.pdf3.84 MBAdobe PDFThumbnail
Preview File