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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  
Self-training semi-supervised classification based on density peaks of data 期刊论文
NEUROCOMPUTING, 2018, 卷号: 275, 页码: 180-191
作者:  Wu, Di;  Shang, Mingsheng;  Luo, Xin;  Xu, Ji;  Yan, Huyong;  Deng, Weihui;  Wang, Guoyin
收藏  |  浏览/下载:169/0  |  提交时间:2018/03/05
Density peaks  Self-training  Semi-supervised classification  Supervised learning  
On minimizing total energy consumption in the scheduling of virtual machine reservations 期刊论文
Journal of Network and Computer Applications, 2018, 卷号: 113, 页码: 64-74
作者:  Tian, Wenhong;  He, Majun;  Guo, Wenxia;  Huang, Wenqiang;  Shi, Xiaoyu;  Shang, Mingsheng;  Toosi, Adel Nadjaran;  Buyya, Rajkumar
Adobe PDF(4775Kb)  |  收藏  |  浏览/下载:140/0  |  提交时间:2019/06/26
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
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:888/1  |  提交时间:2018/03/15
Latent factors  non-negativity  matrix factorization  non-negative big sparse matrix  big data  recommender system