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重庆绿色智能技术研究... [9]
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会议论文 [18]
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Convergence analysis of a fast non-negative latent factor model
会议论文
Bari, Italy, October 6, 2019 - October 9, 2019
作者:
Zhou, Yue
;
Liu, Zhigang
;
Yu, Xiaojiang
;
Wu, Yajuan
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2020/02/18
A parallelized, momentum-incorporated stochastic gradient descent scheme for latent factor analysis on high-dimensional and sparse matrices from recommender systems
会议论文
Bari, Italy, October 6, 2019 - October 9, 2019
作者:
Qin, Wen
;
Wu, Hao
;
Lai, Qingkuan
;
Wang, Chaobin
收藏
  |  
浏览/下载:0/0
  |  
提交时间:2020/02/18
Convergence analysis of an SLF-NMU algorithm for non-negative latent factor analysis on a high-dimensional and sparse matrix
会议论文
Bari, Italy, October 6, 2019 - October 9, 2019
作者:
Liu, Zhigang
;
Luo, Xin
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  |  
浏览/下载:0/0
  |  
提交时间:2020/02/18
Elastic net-regularized latent factor model for recommender systems
会议论文
Zhuhai, China, March 27, 2018 - March 29, 2018
作者:
Cheng, Xi
;
Luo, Xin
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  |  
浏览/下载:26/0
  |  
提交时间:2019/06/25
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications
会议论文
Zhuhai, China, March 27, 2018 - March 29, 2018
作者:
Chen, Jia
;
Luo, Xin
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  |  
浏览/下载:11/0
  |  
提交时间:2019/06/25
Unconstrained Non-negative Factorization of High-dimensional and Sparse Matrices in Recommender Systems
会议论文
Munich, Germany, August 20, 2018 - August 24, 2018
作者:
Luo, Xin
;
Zhou, Mengchu
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  |  
浏览/下载:19/0
  |  
提交时间:2019/06/25
Latent factor analysis for low-dimensional implicit preference prediction
会议论文
4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017, 2017-10-16
作者:
Zhou, Zili[1]
;
Xu, Guandong[2]
;
Zhu, Xiao[3]
;
Liu, Shaowu[4]
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  |  
浏览/下载:9/0
  |  
提交时间:2019/04/22
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications
会议论文
ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
作者:
Chen, J.
;
Luo, X.
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  |  
浏览/下载:7/0
  |  
提交时间:2019/12/30
Computational efficiency
Iterative methods
Learning systems
Matrix algebra
Neural networks
Computational burden
Latent factor analysis
Latent factor models
Learning techniques
Prediction accuracy
Randomized Learning
Sparse matrices
State of the art
Data mining
Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications
会议论文
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), 2018-01-01
作者:
Chen, Jia
;
Luo, Xin
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/30
High-Dimensional and Sparse Matrix
Latent Factor Analysis
Randomized Learning
Neural Network
Latent Factor Analysis for Low-dimensional Implicit Preference Prediction
会议论文
PROCEEDINGS OF 4TH INTERNATIONAL CONFERENCE ON BEHAVIORAL, ECONOMIC ADVANCE IN BEHAVIORAL, ECONOMIC, SOCIOCULTURAL COMPUTING (BESC), 2017-01-01
作者:
Zhou, Zili[1]
;
Xu, Guandong[2]
;
Zhu, Xiao[3]
;
Liu, Shaowu[4]
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/04/24
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