中国科学院重庆绿色智能技术研究院机构知识库

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发表日期：2021

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	Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data 期刊论文 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2021, 卷号: 18, 期号: 4, 页码: 2142-2155 作者:  Luo, Xin;  Chen, Minzhi;  Wu, Hao;  Liu, Zhigang;  Yuan, Huaqiang;  Zhou, Mengchu 收藏  |  浏览/下载：83/0  |  提交时间：2021/11/26 Tensors  Data models  Quality of service  Computational modeling  Analytical models  Training  Web services  Algorithm  big data  dynamics  high-dimensional and incomplete (HDI) data  machine learning  missing data estimation  multichannel data  nonnegative latent factorization of tensors (NLFT)  temporal pattern  quality of service (QoS)  web service
	Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 2, 页码: 916-926 作者:  Luo, Xin;  Wang, Dexian;  Zhou, MengChu;  Yuan, Huaqiang 收藏  |  浏览/下载：67/0  |  提交时间：2021/03/17 Big data  bi-linear  collaborative filtering (CF)  high-dimensional and sparse (HiDS) matrix  industry  latent factor (LF) analysis  missing data  recommender system
	Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems 期刊论文 NEUROCOMPUTING, 2021, 卷号: 421, 页码: 316-328 作者:  Chen, Jiufang;  Yuan, Ye;  Ruan, Tao;  Chen, Jia;  Luo, Xin 收藏  |  浏览/下载：75/0  |  提交时间：2021/02/24 Big Data  Intelligent Computation  Latent Factor Analysis  Evolutionary Computing  Learning Algorithm  High-dimensional and Sparse Data  Parameter Free
	A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 1, 页码: 610-620 作者:  Luo, Xin;  Liu, Zhigang;  Li, Shuai;  Shang, Mingsheng;  Wang, Zidong 收藏  |  浏览/下载：101/0  |  提交时间：2021/03/17 Big data  high-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  missing data estimation  non-negative LF (NLF) model  recommender system