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	An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs
	Li, H; Li, KQ; An, JY; Zheng, WH; Li, KL
刊名	INFORMATION SCIENCES
	2019
卷号	Vol.496页码:464-484
关键词	Collaborative filtering recommender systems Data mining Euclidean distance and KL-divergence GPU parallelization Manifold regularization Non-negative matrix factorization
ISSN号	0020-0255;1872-6291
URL标识	查看原文
公开日期	[db:dc_date_available]
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/4749687
专题	湖南大学
作者单位	1.Hunan Univ, Natl Supercomp Ctr Changsha, Sch Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China 2.SUNY Coll New Paltz, Dept Comp Sci, New Paltz, NY 12561 USA 3.Hunan Univ Technol, Coll Elect & Informat Engn, Changsha 412007, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Li, H,Li, KQ,An, JY,et al. An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs[J]. INFORMATION SCIENCES,2019,Vol.496:464-484.
APA	Li, H,Li, KQ,An, JY,Zheng, WH,&Li, KL.(2019).An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs.INFORMATION SCIENCES,Vol.496,464-484.
MLA	Li, H,et al."An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs".INFORMATION SCIENCES Vol.496(2019):464-484.

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