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Ming-Sheng Shang
Ming-Sheng Shang
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences
Verified email at cigit.ac.cn
Title
Cited by
Cited by
Year
Identifying influential nodes in complex networks
D Chen, L Lü, MS Shang, YC Zhang, T Zhou
Physica a: Statistical mechanics and its applications 391 (4), 1777-1787, 2012
9602012
User-based collaborative-filtering recommendation algorithms on hadoop
ZD Zhao, MS Shang
2010 third international conference on knowledge discovery and data mining …, 2010
4982010
Detecting overlapping communities of weighted networks via a local algorithm
D Chen, M Shang, Z Lv, Y Fu
Physica A: Statistical Mechanics and its Applications 389 (19), 4177-4187, 2010
1572010
An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications
X Luo, MC Zhou, S Li, M Shang
IEEE Transactions on Industrial Informatics 14 (5), 2011-2022, 2017
1532017
Empirical analysis of web-based user-object bipartite networks
MS Shang, L Lü, YC Zhang, T Zhou
EPL (Europhysics Letters) 90 (4), 48006, 2010
1472010
Symmetric and nonnegative latent factor models for undirected, high-dimensional, and sparse networks in industrial applications
X Luo, J Sun, Z Wang, S Li, M Shang
IEEE Transactions on Industrial Informatics 13 (6), 3098-3107, 2017
1122017
Collaborative filtering with diffusion-based similarity on tripartite graphs
MS Shang, ZK Zhang, T Zhou, YC Zhang
Physica A: Statistical Mechanics and its Applications 389 (6), 1259-1264, 2010
1122010
Algorithms of unconstrained non-negative latent factor analysis for recommender systems
X Luo, M Zhou, S Li, D Wu, Z Liu, M Shang
IEEE Transactions on Big Data 7 (1), 227-240, 2019
1002019
A deep latent factor model for high-dimensional and sparse matrices in recommender systems
D Wu, X Luo, M Shang, Y He, G Wang, MC Zhou
IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (7), 4285-4296, 2019
982019
A fast non-negative latent factor model based on generalized momentum method
X Luo, Z Liu, S Li, M Shang, Z Wang
IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (1), 610-620, 2018
952018
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications
M Shang, X Luo, Z Liu, J Chen, Y Yuan, MC Zhou
IEEE/CAA Journal of Automatica Sinica 6 (1), 131-141, 2018
842018
A fast and efficient heuristic algorithm for detecting community structures in complex networks
D Chen, Y Fu, M Shang
Physica A: Statistical Mechanics and its Applications 388 (13), 2741-2749, 2009
832009
Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications
X Luo, MC Zhou, S Li, L Hu, M Shang
IEEE transactions on cybernetics 50 (5), 1844-1855, 2019
782019
Empirical Analysis on the Human Dynamics of a Large-Scale Short Message Communication System
ZD Zhao, H Xia, MS Shang, T Zhou
Chinese Physics Letters 28 (6), 068901, 2011
772011
A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices.
X Luo, M Zhou, M Shang, S Li, Y Xia
IEEE Access 4, 2649 - 2655, 2016
712016
Self-training semi-supervised classification based on density peaks of data
D Wu, M Shang, X Luo, J Xu, H Yan, W Deng, G Wang
Neurocomputing 275, 180-191, 2018
692018
Can dissimilar users contribute to accuracy and diversity of personalized recommendation?
W Zeng, MS Shang, QM Zhang, L Lü, T Zhou
International Journal of Modern Physics C 21 (10), 1217-1227, 2010
672010
Relevance is more significant than correlation: Information filtering on sparse data
MS Shang, L Lü, W Zeng, YC Zhang, T Zhou
EPL (Europhysics Letters) 88 (6), 68008, 2010
642010
A data-characteristic-aware latent factor model for web services QoS prediction
D Wu, X Luo, M Shang, Y He, G Wang, X Wu
IEEE Transactions on Knowledge and Data Engineering, 2020
632020
A highly accurate framework for self-labeled semisupervised classification in industrial applications
D Wu, X Luo, G Wang, M Shang, Y Yuan, H Yan
IEEE Transactions on Industrial Informatics 14 (3), 909-920, 2017
592017
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