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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限定条件	资助项目：National Natural Science Foundation of China[61702475]    作者：尚明生    第一作者

已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择期刊影响因子升序期刊影响因子降序作者升序作者降序题名升序题名降序WOS被引频次升序WOS被引频次降序发表日期升序发表日期降序提交时间升序提交时间降序
	A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction 期刊论文 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 卷号: 34, 期号: 6, 页码: 2525-2538 作者:  Wu, Di;  Luo, Xin;  Shang, Mingsheng;  He, Yi;  Wang, Guoyin;  Wu, Xindong 收藏  |  浏览/下载：67/0  |  提交时间：2022/08/22 Web Service  quality-of-service  QoS  latent factor analysis  density peak  data-characteristic-aware  missing data  big data  topological neighborhood  noise data  service selection  data science
	Large-Scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems 期刊论文 IEEE TRANSACTIONS ON BIG DATA, 2022, 卷号: 8, 期号: 2, 页码: 420-431 作者:  Shi, Xiaoyu;  He, Qiang;  Luo, Xin;  Bai, Yanan;  Shang, Mingsheng 收藏  |  浏览/下载：72/0  |  提交时间：2022/08/22 Recommender systems  Training  Optimization  Big Data  Cloud computing  Computational modeling  Sparse matrices  Recommender system  latent factor analysis  high-dimensional and sparse matrices  alternative stochastic gradient descent  distributed computing
	Non-Negative Latent Factor Model Based on beta-Divergence for Recommender Systems 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 8, 页码: 4612-4623 作者:  Xin, Luo;  Yuan, Ye;  Zhou, MengChu;  Liu, Zhigang;  Shang, Mingsheng 收藏  |  浏览/下载：130/0  |  提交时间：2021/08/20 beta-divergence  big data  high-dimensional and sparse (HiDS) matrix  industrial application  learning algorithm  non-negative latent factor (NLF) analysis  recommender system
	An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 14 作者:  Wu, Di;  Shang, Mingsheng;  Luo, Xin;  Wang, Zidong 收藏  |  浏览/下载：42/0  |  提交时间：2022/08/22 High-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  L-1 norm  L-2 norm  recommender system (RS)
	A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications 期刊论文 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 3, 页码: 909-920 作者:  Wu, Di;  Luo, Xin;  Wang, Guoyin;  Shang, Mingsheng;  Yuan, Ye;  Yan, Huyong 收藏  |  浏览/下载：213/0  |  提交时间：2018/06/04 Differential evolution (DE)  general framework  industrial application  positioning optimization  self-labeled  semi-supervised classification (SSC)