基本信息
尚明生  男  博导  中国科学院重庆绿色智能技术研究院
电子邮件: msshang@cigit.ac.cn
通信地址: 重庆北碚区水土镇水土高新园方正大道266号
邮政编码: 400714

研究领域

大数据智能计算及应用

招生信息


招生专业
081203-计算机应用技术
081202-计算机软件与理论
招生方向
大数据挖掘
智能信息处理
推荐系统

教育背景

2003-09--2007-12   电子科技大学   博士
2000-09--2003-05   电子科技大学   硕士

工作经历

   
工作简历
2015-07~现在, 中科院重庆绿色智能技术研究院, 研究员
2014-08~2015-01,瑞士弗里堡大学, 访问学者
2011-08~2011-10,美国罗切斯特大学, 访问学者
2010-08~2015-07,电子科技大学, 教授,博士生导师
2007-12~2009-01,美国明尼苏达大学, 访问学者
2002-02~2010-07,电子科技大学, 讲师/副教授

专利与奖励

   
奖励信息
(1) 智慧金融集成生物识别关键技术及应用, 一等奖, 省级, 2018
(2) 智慧金融中的集成生物识别关键技术及应用, 一等奖, 其他, 2018
(3) 国务院政府特殊津贴, 国家级, 2018
专利成果
[1] 林远长, 汪凌峰, 何国田, 尚明生. 一种应用于手术机器人的器械和设备. CN: CN110664486B, 2022-02-08.
[2] 史晓雨, 尚明生. 一种融合用户偏好预测的深度强化学习推荐方法. CN202111519219.9, 2021-12-06.
[3] 林远长, 代康, 何国田, 何玉泽, 刘东, 尚明生. 一种力敏传感单元磁致链化实时成链控制装置和方法. CN: CN113359944A, 2021-09-07.
[4] 张学睿, 尚明生, 张帆, 姚远, 郑志浩. 一种基于三阶级联架构的YOLOv3的远景目标检测方法. CN: CN113239813A, 2021-08-10.
[5] 林远长, 唐晨, 何国田, 尚明生, 刘东. 一种智能喷涂机器人系统及其喷涂方法. CN: CN112642619A, 2021-04-13.
[6] 尚明生, 刘梅, 彭波, 张嘉政. 一种面向通信时滞的机械臂系统多级优化协调控制方法. CN: CN112621761A, 2021-04-09.
[7] 林远长, 崔怀丰, 何国田, 陈光平, 尚明生, 代康, 刘东, 张振军. 具有力感知能力的圆柱体夹持装置. CN: CN112621791A, 2021-04-09.
[8] 林远长, 刘东, 代康, 何玉泽, 何国田, 尚明生. 一种磁流变弹性体变阻器. CN: CN112582120A, 2021-03-30.
[9] 刘东, 林远长, 刘宗辉, 何国田, 尚明生. 一种从额头温度估计体核温度的方法及其应用. CN: CN112487692A, 2021-03-12.
[10] 刘东, 林远长, 何国田, 刘宗辉, 尚明生. 一种红外热成像相机标定装置. CN: CN112465919A, 2021-03-09.
[11] 袁野, 许明, 罗辛, 尚明生. 一种Web服务吞吐量时变隐特征分析装置和方法. CN: CN112131080A, 2020-12-25.
[12] 张能锋, 袁野, 罗辛, 尚明生. 一种基于多层随机隐特征模型的网页广告投放装置和方法. CN: CN112036963A, 2020-12-04.
[13] 陈琳, 郑小强, 尚明生, 朱帆. 基于生成对抗学习的行人属性识别方法. CN: CN112016490A, 2020-12-01.
[14] 史晓雨, 尚明生, 王思源. 一种对抗攻击敏感的文本分类方法. CN: CN111984762A, 2020-11-24.
[15] 张学睿, 尚明生, 张帆, 姚远, 郑志浩. 一种基于DIOU损失函数的训练网络的方法. CN: CN111931915A, 2020-11-13.
[16] 陈琳, 尚明生, 朱帆. 基于对抗学习的化合物图像分子结构式提取方法. CN: CN111860507A, 2020-10-30.
[17] 周博天, 尚明生, 闪锟, 马健荣, 封雷. 一种水华期藻类群落结构高光谱识别方法. CN: CN111795941A, 2020-10-20.
[18] 陈琳, 宋小军, 尚明生, 朱帆. 一种自适应人群计数系统及自适应人群计数方法. CN: CN111639585A, 2020-09-08.
[19] 姚远, 郑志浩, 张学睿, 张帆, 尚明生. 一种小样本下复杂环境的目标识别方法. CN: CN111582345A, 2020-08-25.
[20] 郑志浩, 姚远, 张学睿, 张帆, 尚明生. 一种基于稀疏样本的视频压缩方法. CN: CN111565318A, 2020-08-21.
[21] 史晓雨, 尚明生, 罗梦珍, 白亚男. 一种面向不平衡文本数据的自分类方法. CN: CN110609898A, 2019-12-24.
[22] 史晓雨, 尚明生, 吕元鑫, 冉龙玉. 一种混凝土生产配合比的智能设计方法. CN: CN110435009A, 2019-11-12.
[23] 陈琳, 彭彬彬, 尚明生, 朱帆. 一种超高像素的组织病理图像分割方法. CN: CN110288613A, 2019-09-27.
[24] 史晓雨, 尚明生, 吕元鑫. 一种混凝土28d抗压强度预测方法. CN: CN110263431A, 2019-09-20.
[25] 朱帆, 尚明生, 陈琳. 基于相关系数的中风灌注成像病变区域检测系统及方法. CN: CN110236544A, 2019-09-17.
[26] 罗辛, 吴昊, 陈敏治, 尚明生, 刘志刚, 钟裕荣. 一种基于偏置张量分解的云服务响应时间预测方法和装置. CN: CN110113180A, 2019-08-09.
[27] 罗辛, 吴昊, 尚明生, 陈敏治, 钟裕荣, 王德贤. 一种时序网络动态隐特征抽取方法和装置. CN: CN110083631A, 2019-08-02.
[28] 史晓雨, 冀倩倩, 尚明生. 一种不完备专利自动标引方法. CN: CN109726299A, 2019-05-07.
[29] 史晓雨, 尚明生, 白亚男. 一种高效能数据中心云服务器资源自主管理方法和系统. CN: CN109491760A, 2019-03-19.
[30] 田文洪, 王金, 何博, 叶宇飞, 尚明生, 史晓雨. 一种基于深度强化学习的资源调度方法和系统. CN: CN108595267A, 2018-09-28.
[31] 田文洪, 黄超杰, 王金, 尚明生. 一种解决Spark数据倾斜问题的负载均衡方法及装置. CN: CN108572873A, 2018-09-25.
[32] 袁野, 李超华, 罗辛, 尚明生, 吴迪. 一种视频数据线性偏差主特征提取装置和方法. CN: CN107808163A, 2018-03-16.
[33] 袁野, 罗辛, 尚明生, 吴迪. 一种视频数据多维非负隐特征的提取装置和方法. CN: CN107704830A, 2018-02-16.
[34] 封丽, 封雷, 李崇明, 尚明生, 周博天, 闪坤, 程艳茹, 张君, 刘鑫, 刘异齐, 张韵, 史晓雨. 一种基于用户优先度的遥感分发方法及系统. CN: CN107104956A, 2017-08-29.
[35] 王国胤, 董建华, 尚明生, 严胡勇, 王浩林, 郑志浩, 史晓雨. 基于邻域粗糙集和PCA融合的数据分类预测方法. CN: CN107016416A, 2017-08-04.
[36] 吴迪, 李超华, 尚明生, 罗辛, 袁野. 一种基于数据密度峰值的自标记半监督分类方法及装置. CN: CN106778859A, 2017-05-31.
[37] 史晓雨, 尚明生, 田文洪, 罗辛. 一种能耗感知的云计算服务器资源在线管理方法和系统. CN: CN106648890A, 2017-05-10.
[38] 王国胤, 徐计, 邓伟辉, 尚明生, 张学睿. 一种基于密度峰值的高效层次聚类方法. CN: CN105631465A, 2016-06-01.
[39] 尚明生, 李锴, 张航. 一种微博转发量预测方法. CN: CN105550275A, 2016-05-04.
[40] 尚明生, 史晓雨. 一种基于用户自主选择的个性化推荐方法和系统. CN: CN105512183A, 2016-04-20.
[41] 张帆, 张学睿, 王国胤, 尚明生. 一种海量动态数据管理方法. CN: CN105426506A, 2016-03-23.
[42] 尚明生, 李健, 史晓雨. 一种在线系统中用户定制推荐系统的方法. CN: CN105404678A, 2016-03-16.
[43] 王国胤, 田亚兰, 徐计, 尚明生, 张学睿. 一种非线性对流扩散方程的粒计算加速求解方法. CN: CN105224504A, 2016-01-06.

出版信息

发表论文
[1] Liu, Mei, Zhang, Xiaoyan, Shang, Mingsheng, Jin, Long. Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots. IEEE-CAA JOURNAL OF AUTOMATICA SINICA[J]. 2022, 9(8): 1452-1463, [2] Wu, Di, Luo, Xin, Shang, Mingsheng, He, Yi, Wang, Guoyin, Wu, Xindong. A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING[J]. 2022, 34(6): 2525-2538, http://dx.doi.org/10.1109/TKDE.2020.3014302.
[3] Li, Qing, Xiong, Diwen, Shang, Mingsheng. Adjusted stochastic gradient descent for latent factor analysis. INFORMATION SCIENCES[J]. 2022, 588: 196-213, http://dx.doi.org/10.1016/j.ins.2021.12.065.
[4] Shang, Mingsheng, Yuan, Ye, Luo, Xin, Zhou, MengChu. An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences. IEEE TRANSACTIONS ON CYBERNETICS[J]. 2022, 52(8): 8006-8018, [5] Liu, Mei, Zhang, Jiazheng, Shang, Mingsheng. Real-time cooperative kinematic control for multiple robots in distributed scenarios with dynamic neural networks. NEUROCOMPUTING[J]. 2022, 491: 621-632, http://dx.doi.org/10.1016/j.neucom.2021.12.038.
[6] Liu, Mei, Shang, Mingsheng. On RNN-Based k-WTA Models With Time-Dependent Inputs. IEEE-CAA JOURNAL OF AUTOMATICA SINICAnull. 2022, 9(11): 2034-2036, http://dx.doi.org/10.1109/JAS.2022.105932.
[7] Wu, Di, He, Qiang, Luo, Xin, Shang, Mingsheng, He, Yi, Wang, Guoyin. A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction. IEEE TRANSACTIONS ON SERVICES COMPUTING[J]. 2022, 15(2): 793-805, http://dx.doi.org/10.1109/TSC.2019.2961895.
[8] Wu, Di, Shang, Mingsheng, Luo, Xin, Wang, Zidong. An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS[J]. 2022, 33(10): 5775-5788, http://dx.doi.org/10.1109/TNNLS.2021.3071392.
[9] Liu, Mei, Chen, Liangming, Du, Xiaohao, Jin, Long, Shang, Mingsheng. Activated Gradients for Deep Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS[J]. 2021, http://dx.doi.org/10.1109/TNNLS.2021.3106044.
[10] Liu, Mei, He, Li, Shang, Mingsheng. Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators. IEEE Transactions on Neural Networks and Learning Systems[J]. 2021, [11] Peng, Bo, Shang, Mingsheng, Jin, Long. Multi-robot competitive tracking based on k-WTA neural network with one single neuron. NEUROCOMPUTING[J]. 2021, 460: 1-8, http://apps.webofknowledge.com/CitedFullRecord.do?product=UA&colName=WOS&SID=5CCFccWmJJRAuMzNPjj&search_mode=CitedFullRecord&isickref=WOS:000696919200001.
[12] Luo, Xin, Liu, Zhigang, Li, Shuai, Shang, Mingsheng, Wang, Zidong. A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS[J]. 2021, 51(1): 610-620, http://dx.doi.org/10.1109/TSMC.2018.2875452.
[13] Luo, Xin, Liu, Zhigang, Shang, Mingsheng, Lou, Jungang, Zhou, MengChu. Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING[J]. 2021, 8(1): 463-476, http://dx.doi.org/10.1109/TNSE.2020.3040407.
[14] Li, Qing, Shang, Mingsheng. BALFA: A brain storm optimization-based adaptive latent factor analysis model. INFORMATION SCIENCES[J]. 2021, 578: 913-929, http://apps.webofknowledge.com/CitedFullRecord.do?product=UA&colName=WOS&SID=5CCFccWmJJRAuMzNPjj&search_mode=CitedFullRecord&isickref=WOS:000701110700012.
[15] Mingsheng Shang, Ye Yuan, Xin Luo, MengChu Zhou. An α-β-divergence-generalized recommender for highly accurate predictions of missing user preferences. IEEE Transactions on Cybernetics[J]. 2021, [16] Zhou, Leming, Li,Qin, Shang,MingSheng. Chronic Disease Detection Via Non-negative Latent Feature Analysis. 18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021[J]. 2021, [17] Luo, Xin, Zhou, Mengchu, Li, Shuai, Wu, Di, Liu, Zhigang, Shang, Mingsheng. Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems. IEEE TRANSACTIONS ON BIG DATA[J]. 2021, 7(1): 227-240, http://dx.doi.org/10.1109/TBDATA.2019.2916868.
[18] Liu, Mei, Zhang, Xiaoyan, Shang, Mingsheng. Computational Neural Dynamics Model for Time-Variant Constrained Nonlinear Optimization Applied to Winner-Take-All Operation. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS[J]. 2021, 18(9): 5936-5948, [19] Luo, Xin, Wang, Zidong, Shang, Mingsheng. An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS[J]. 2021, 51(6): 3522-3532, http://dx.doi.org/10.1109/TSMC.2019.2930525.
[20] Yuan, Ye, He, Qiang, Luo, Xin, Shang, Mingsheng. A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices. IEEE Transactions on Big Data[J]. 2020, 8(3): 784-794, [21] Shi, Xiaoyu, He, Qiang, Luo, Xin, Shang, Mingsheng. Large-scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems. IEEE Transaction on Big Data[J]. 2020, 8(2): 420-431, [22] Yuan, Ye, Luo, Xin, Shang, Mingsheng, Wu, Di. A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems. WWW 2020null. 2020, [23] Zhong, Yurong, Jin, Long, Shang, Mingsheng, Luo, Xin. Momentum-incorporated Symmetric Non-negative Latent Factor Models. IEEE Transactions on Big Data[J]. 2020, 8(4): 1096-1106, [24] Wu, Di, Luo, Xin, Shang, Mingsheng, He, Yi, Wang, Guoyin, Wu, Xindong. A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction. IEEE Transactions on Knowledge and Data Engineering[J]. 2020, 34(6): 2525-2538, [25] Zhou, Botian, Shang, Mingsheng, Feng, Li, Shan, Kun, Feng, Lei, Ma, Jianrong, Liu, Xiangnan, Wu, Ling. Long-term remote tracking the dynamics of surface water turbidity using a density peaks -based classification: A case study in the Three Gorges Reservoir, China. ECOLOGICAL INDICATORS[J]. 2020, 116: http://dx.doi.org/10.1016/j.ecolind.2020.106539.
[26] Luo, Xin, Zhou, MengChu, Li, Shuai, Hu, Lun, Shang, Mingsheng. Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications. IEEE TRANSACTIONS ON CYBERNETICS[J]. 2020, 50(5): 1844-1855, http://dx.doi.org/10.1109/TCYB.2019.2894283.
[27] Wu, Di, He, Qiang, Luo, Xin, Shang, Mingsheng, He, Yi, Wang, Guoyin. A Posterior-neighborhood-regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction. IEEE Transactions on Services Computing[J]. 2019, 15(2): 793-805, [28] Shang, Mingsheng, Luo, Xin, Liu, Zhigang, Chen, Jia, Yuan, Ye, Zhou, MengChu. Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications. IEEE-CAA JOURNAL OF AUTOMATICA SINICA[J]. 2019, 6(1): 131-141, http://lib.cqvip.com/Qikan/Article/Detail?id=90687266504849574849484949.
[29] Shan Kun, Shang Mingsheng, Zhou Botian, Li Lin, Wang Xiaoxiao, Yang Hong, Song Lirong. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. 2019, http://119.78.100.158/handle/2HF3EXSE/130843.
[30] Wang, Qingxian, Chen, Minzhi, Shang, Mingsheng, Luo, Xin. A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction. NEUROCOMPUTING[J]. 2019, 367: 299-307, http://dx.doi.org/10.1016/j.neucom.2019.08.026.
[31] Wu, Di, He, Yi, Luo, Xin, Shang, Mingsheng, Wu, Xindong, Baru, C, Huan, J, Khan, L, Hu, XH, Ak, R, Tian, Y, Barga, R, Zaniolo, C, Lee, K, Ye, YF. Online Feature Selection with Capricious Streaming Features: A General Framework. 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)null. 2019, 683-688, [32] Shan, Kun, Shang, Mingsheng, Zhou, Botian, Li, Lin, Wang, Xiaoxiao, Yang, Hong, Song, Lirong. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. HARMFUL ALGAE[J]. 2019, 83(1): 14-24, http://dx.doi.org/10.1016/j.hal.2019.01.005.
[33] Zhou, Botian, Shang, Mingsheng, Zhang, Sheng, Feng, Li, Li, Xiangnan, Wu, Ling, Feng, Lei, Shan, Kun. Remote examination of the seasonal succession of phytoplankton assemblages from time-varying trends. JOURNAL OF ENVIRONMENTAL MANAGEMENT[J]. 2019, 246: 687-694, http://dx.doi.org/10.1016/j.jenvman.2019.06.035.
[34] Yuan, Ye, Luo, Xin, Shang, MingSheng. Effects of preprocessing and training biases in latent factor models for recommender systems. NEUROCOMPUTING[J]. 2018, 275: 2019-2030, http://dx.doi.org/10.1016/j.neucom.2017.10.040.
[35] Luo, Xin, Zhou, MengChu, Li, Shuai, Shang, MingSheng. An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS[J]. 2018, 14(5): 2011-2022, http://119.78.100.138/handle/2HOD01W0/8021.
[36] Wu, Di, Luo, Xin, Wang, Guoyin, Shang, Mingsheng, Yuan, Ye, Yan, Huyong. A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS[J]. 2018, 14(3): 909-920, http://dx.doi.org/10.1109/TII.2017.2737827.
[37] Wang, QingXian, Luo, Xin, Li, Yan, Shi, XiaoYu, Gu, Liang, Shang, MingSheng. Incremental Slope-one recommenders. NEUROCOMPUTING[J]. 2018, 272: 606-618, http://dx.doi.org/10.1016/j.neucom.2017.07.033.
[38] Zhou, Botian, Shang, Mingsheng, Wang, Guoyin, Zhang, Sheng, Feng, Li, Liu, Xiangnan, Wu, Ling, Shan, Kun. Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI). SCIENCE OF THE TOTAL ENVIRONMENT[J]. 2018, 628-629: 848-857, http://dx.doi.org/10.1016/j.scitotenv.2018.02.097.
[39] Tian, Wenhong, He, Majun, Guo, Wenxia, Huang, Wenqiang, Shi, Xiaoyu, Shang, Mingsheng, Toosi, Adel Nadjaran, Buyya, Rajkumar. On minimizing total energy consumption in the scheduling of virtual machine reservations. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS[J]. 2018, 113: 64-74, http://119.78.100.138/handle/2HOD01W0/8051.
[40] Wu Di, Shang Mingsheng, Wang Guoyin, Li Li. A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution. 15TH IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL, ICNSC 2018null. 2018, 1-6, http://119.78.100.138/handle/2HOD01W0/7953.
[41] Wu, Di, Shang, Mingsheng, Luo, Xin, Xu, Ji, Yan, Huyong, Deng, Weihui, Wang, Guoyin. Self-training semi-supervised classification based on density peaks of data. NEUROCOMPUTING[J]. 2018, 275: 180-191, http://dx.doi.org/10.1016/j.neucom.2017.05.072.
[42] Abbas, Khushnood, Shang, Mingsheng, Abbasi, Alireza, Luo, Xin, Xu, Jian Jun, Zhang, YuXia. Popularity and Novelty Dynamics in Evolving Networks. SCIENTIFIC REPORTS[J]. 2018, 8(1): https://doaj.org/article/86d5d46f478d40fdba436bd5559ac4c9.
[43] Shi, Xiaoyu, Luo, Xin, Shang, Mingsheng, Gu, Liang. Long-term performance of collaborative filtering based recommenders in temporally evolving systems. NEUROCOMPUTING[J]. 2017, 267: 635-643, http://dx.doi.org/10.1016/j.neucom.2017.06.026.
[44] Luo Xin, Shang MingSheng. Symmetric non-negative latent factor models for undirected large networks. 26THINTERNATIONALJOINTCONFERENCEONARTIFICIALINTELLIGENCEIJCAI2017null. 2017, 2435-2442, http://www.chinair.org.cn/handle/1471x/1660739.
[45] Wu, Di, Luo, Xin, Wang, Guoyin, Shang, Mingsheng, Yuan, Ye, Yan, Huyong. A Highly-Accurate Framework for Self-Labeled Semi-Supervised Classification in Industrial Applications.. IEEE Transactions on Industrial Informatics[J]. 2017, 14(3): 909-920, [46] Chen, Jia, Luo, Xin, Yuan, Ye, Shang, Mingsheng, Ming, Zhong, Xiong, Zhang. Performance of latent factor models with extended linear biases. KNOWLEDGE-BASED SYSTEMS[J]. 2017, 123: 128-136, http://dx.doi.org/10.1016/j.knosys.2017.02.010.
[47] Zhou, Botian, Shang, Mingsheng, Wang, Guoyin, Feng, Li, Shan, Kun, Liu, Xiangnan, Wu, Ling, Zhang, Xuerui. Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH[J]. 2017, 24(23): 19044-19056, https://www.webofscience.com/wos/woscc/full-record/WOS:000407723100027.
[48] Wu, Di, Yan, Huyong, Shang, Mingsheng, Shan, Kun, Wang, Guoyin. Water eutrophication evaluation based on semi-supervised classification: A case study in Three Gorges Reservoir. ECOLOGICAL INDICATORS[J]. 2017, 81: 362-372, http://dx.doi.org/10.1016/j.ecolind.2017.06.004.
[49] 尚明生. 推荐系统:从个性化算法到算法的个性化. 西华师范大学学报:自然科学版[J]. 2016, 37(1): 61-66+3, http://lib.cqvip.com/Qikan/Article/Detail?id=668612200.
[50] Luo Xin, Shang Mingsheng, Li Shuai, Bonchi F, DomingoFerrer J, BaezaYates R, Zhou ZH, Wu X. Efficient Extraction of Non-negative Latent Factors from High-dimensional and Sparse Matrices in Industrial Applications. 2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)null. 2016, 311-319, [51] 段杰明, 尚明生, 蔡世民, 张玉霞. 基于自规避随机游走的节点排序算法 (EI收录). 《物理学报》[J]. 2015, 61-68, http://www.corc.org.cn/handle/1471x/2210122.
[52] Zeng, Wei, Zeng, An, Liu, Hao, Shang, MingSheng, Zhang, YiCheng. Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering. PLOS ONE[J]. 2014, 9(10): https://doaj.org/article/526ebd8abab244ed9b72164ddbe8895e.
[53] Guan, Yuan, Cai, Shimin, Shang, Mingsheng. Recommendation algorithm based on item quality and user rating preferences. FRONTIERS OF COMPUTER SCIENCE[J]. 2014, 8(2): 289-297, https://www.webofscience.com/wos/woscc/full-record/WOS:000334183200011.
[54] Guan, Yuan, Zhao, Dandan, Zeng, An, Shang, MingSheng. Preference of online users and personalized recommendations. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS[J]. 2013, 392(16): 3417-3423, http://dx.doi.org/10.1016/j.physa.2013.03.045.
[55] Zhang, QianMing, Zeng, An, Shang, MingSheng. Extracting the Information Backbone in Online System. PLOS ONE[J]. 2013, 8(5): https://doaj.org/article/351df289b2f74e4cb5295e5abf8be63e.
[56] Zeng, Wei, Zeng, An, Shang, MingSheng, Zhang, YiCheng. Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion. PLOS ONE[J]. 2013, 8(11): https://doaj.org/article/994758794e63406ba9ad832fe83cc778.
发表著作
(1) 网络舆情信息分析与处理技术, WLYX, 科学出版社, 2015-02, 第 1 作者
(2) 水生态环境感知信息分析系统研究及应用, 科学出版社, 2020-10, 第 1 作者

科研活动

   
科研项目
( 1 ) 动态演化在线系统中的信息推荐问题研究, 主持, 国家级, 2014-01--2017-12
( 2 ) 中科院****, 主持, 部委级, 2016-01--2018-12
( 3 ) 大数据结构与关系的度量与简约计算, 参与, 国家级, 2015-01--2018-12
( 4 ) 面向云计算数据中心的低能耗高性能自主计算研究, 主持, 省级, 2016-01--2018-12
( 5 ) 洛丁智慧照明系统开发, 主持, 院级, 2016-01--2025-09
( 6 ) 初等数学问题求解关键技术及系统, 参与, 国家级, 2015-01--2017-12
( 7 ) 大数据基础科研与开发平台建设, 主持, 省级, 2016-04--2016-12
( 8 ) 面向高维稀疏时变数据的宏观趋势预测研究, 主持, 国家级, 2017-01--2019-12
( 9 ) 悦来新城海绵城市监测与信息平台建设, 主持, 院级, 2017-03--2020-12
( 10 ) 慢病创新服务研发及慢病示范应用, 参与, 院级, 2017-09--2018-12
( 11 ) 基于大数据的服务交易关键技术研究与应用示范, 主持, 省级, 2018-01--2019-12
( 12 ) 大数据与智能计算重庆市重点实验室, 主持, 省级, 2016-12--2018-12
( 13 ) 智慧慢病服药(scp)管理工程示范基地建设, 参与, 部委级, 2018-01--2019-06
( 14 ) 医药专利大数据智能分析决策系统与应用示范, 参与, 省级, 2018-01--2019-12
( 15 ) 基于深度学习的长效推荐技术研究, 参与, 国家级, 2019-01--2021-12
( 16 ) 监控检测技术研究, 主持, 国家级, 2019-02--2021-01
( 17 ) 数字城市大数据联合实验室, 主持, 院级, 2019-10--2022-10
( 18 ) 面向海绵城市运维大数据的高维稀疏张量分析方法研究, 主持, 国家级, 2021-01--2024-12
( 19 ) 新药研发大数据平台, 参与, 国家级, 2020-07--2022-06
( 20 ) 智能终端软件, 主持, 国家级, 2020-06--2021-07
参与会议
(1)Research and applications in Bigdata   一带一路地方合作委员会首次大会暨“人工智能助推城市治理”   2018-12-10
(2)大数据智能的研究及应用   大数据智能化前沿科技学术报告会   2018-11-13
(3)大数据时代的信息获取   2018-07-07
(4)Symmetric Non-negative Latent Factor Models for Undirected Large Networks   2017-08-20

指导学生

已指导学生

彭彬彬  硕士研究生  081203-计算机应用技术  

现指导学生

袁野  博士研究生  081203-计算机应用技术  

徐晓宇  博士研究生  081203-计算机应用技术  

王韬  硕士研究生  081203-计算机应用技术  

冉龙宇  硕士研究生  085211-计算机技术