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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  
Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems 期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2021, 卷号: 7, 期号: 1, 页码: 227-240
作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Wu, Di;  Liu, Zhigang;  Shang, Mingsheng
收藏  |  浏览/下载：148/0  |  提交时间：2021/05/17
Data models  Training  Sparse matrices  Recommender systems  Computational modeling  Big Data  Scalability  Non-negative latent factor analysis  non-negativity  latent factor analysis  unconstrained optimization  high-dimensional and sparse matrix  collaborative filtering  recommender system  big data  
DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 60186-60198
作者:  Wang, Qingxian;  Peng, Binbin;  Shi, Xiaoyu;  Shang, Tianqi;  Shang, Mingsheng
Adobe PDF(4751Kb)  |  收藏  |  浏览/下载：144/0  |  提交时间：2019/06/24
Recommender systems  collaborative filtering  rating prediction  denoising autoencoders  multi layered perceptron  
An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, MengChu;  Li, Shuai;  Shang, MingSheng
收藏  |  浏览/下载：508/0  |  提交时间：2018/07/02
Big data  high-dimensional and sparse matrix  learning algorithms  missing-data estimation  nonnegative latent factor analysis  optimization methods recommender system  
An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications 期刊论文
IEEE Transactions on Industrial Informatics, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Shang, Mingsheng
Adobe PDF(805Kb)  |  收藏  |  浏览/下载：416/0  |  提交时间：2019/06/26
Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model 期刊论文
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 卷号: 484, 页码: 506-515
作者:  Abbas, Khushnood;  Shang, Mingsheng;  Luo, Xin;  Abbasi, Alireza
Adobe PDF(2072Kb)  |  收藏  |  浏览/下载：102/0  |  提交时间：2018/03/05
Novelty  Evolving networks  Recommender systems  E-commerce  Collective behaviour  Trend prediction  Emerging behaviour  
A dynamic neural controller for adaptive optimal control of permanent magnet DC motors 会议论文
2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, AK, United states, May 14, 2017 - May 19, 2017
作者:  Zhang, Yinyan;  Li, Shuai;  Luo, Xin;  Shang, Ming-Sheng
Adobe PDF(250Kb)  |  收藏  |  浏览/下载：103/0  |  提交时间：2018/03/16
Effect of linear biases in latent factor models on high-dimensional and sparse matrices from recommender systems 会议论文
14th IEEE International Conference on Networking, Sensing and Control, ICNSC 2017, Calabria, Italy, May 16, 2017 - May 18, 2017
作者:  Yuan, Ye;  Luo, Xin;  Shang, Ming-Sheng;  Cai, Xin-Yi
Adobe PDF(717Kb)  |  收藏  |  浏览/下载：112/0  |  提交时间：2018/03/16
Performance of latent factor models with extended linear biases 期刊论文
Knowledge-Based Systems, 2017, 卷号: 123, 页码: 128-136
作者:  Chen, Jia;  Luo, Xin;  Yuan, Ye;  Shang, Mingsheng;  Ming, Zhong;  Xiong, Zhang
Adobe PDF(2755Kb)  |  收藏  |  浏览/下载：98/0  |  提交时间：2018/03/16
A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices 期刊论文
IEEE ACCESS, 2016, 卷号: 4, 页码: 2649-2655
作者:  Luo, Xin;  Zhou, Mengchu;  Shang, Mingsheng;  Li, Shuai;  Xia, Yunni
Adobe PDF(9487Kb)  |  收藏  |  浏览/下载：881/1  |  提交时间：2018/03/15
Latent factors  non-negativity  matrix factorization  non-negative big sparse matrix  big data  recommender system