尚明生-中国科学院大学-UCAS
[中文]
[English]
研究领域
招生信息
教育背景
工作经历
专利与奖励
出版信息
科研活动
指导学生
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）&nbsp智慧金融集成生物识别关键技术及应用,&nbsp一等奖,&nbsp省级,&nbsp2018（2）&nbsp智慧金融中的集成生物识别关键技术及应用,&nbsp一等奖,&nbsp其他,&nbsp2018（3）&nbsp国务院政府特殊津贴,&nbsp国家级,&nbsp2018
专利成果
[1] 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.&nbspCN202111519219.9,&nbsp2021-12-06.[2] 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.&nbspCN:&nbspCN110113180B,&nbsp2021-11-26.[3] 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.&nbspCN:&nbspCN108572873B,&nbsp2021-08-24.[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.&nbspCN:&nbspCN113239813A,&nbsp2021-08-10.[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.&nbspCN:&nbspCN112642619A,&nbsp2021-04-13.[6] 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.&nbspCN:&nbspCN112582120A,&nbsp2021-03-30.[7] 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.&nbspCN:&nbspCN112487692A,&nbsp2021-03-12.[8] 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.&nbspCN:&nbspCN112465919A,&nbsp2021-03-09.[9] 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.&nbspCN:&nbspCN107016416B,&nbsp2021-02-12.[10] 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.&nbspCN:&nbspCN107808163B,&nbsp2020-12-29.[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.&nbspCN:&nbspCN112131080A,&nbsp2020-12-25.[12] 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.&nbspCN:&nbspCN107704830B,&nbsp2020-12-08.[13] 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.&nbspCN:&nbspCN112036963A,&nbsp2020-12-04.[14] 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.&nbspCN:&nbspCN112016490A,&nbsp2020-12-01.[15] 史晓雨, 尚明生, 王思源. 一种对抗攻击敏感的文本分类方法.&nbspCN:&nbspCN111984762A,&nbsp2020-11-24.[16] 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.&nbspCN:&nbspCN111931915A,&nbsp2020-11-13.[17] 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.&nbspCN:&nbspCN110435009B,&nbsp2020-11-10.[18] 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.&nbspCN:&nbspCN111860507A,&nbsp2020-10-30.[19] 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.&nbspCN:&nbspCN111795941A,&nbsp2020-10-20.[20] 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.&nbspCN:&nbspCN111639585A,&nbsp2020-09-08.[21] 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.&nbspCN:&nbspCN111582345A,&nbsp2020-08-25.[22] 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.&nbspCN:&nbspCN111565318A,&nbsp2020-08-21.[23] 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.&nbspCN:&nbspCN110664486A,&nbsp2020-01-10.[24] 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.&nbspCN:&nbspCN110609898A,&nbsp2019-12-24.[25] 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.&nbspCN:&nbspCN105512183B,&nbsp2019-10-11.[26] 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.&nbspCN:&nbspCN110288613A,&nbsp2019-09-27.[27] 史晓雨, 尚明生, 吕元鑫. 一种混凝土28d抗压强度预测方法.&nbspCN:&nbspCN110263431A,&nbsp2019-09-20.[28] 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.&nbspCN:&nbspCN110236544A,&nbsp2019-09-17.[29] 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.&nbspCN:&nbspCN110083631A,&nbsp2019-08-02.[30] 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.&nbspCN:&nbspCN107104956B,&nbsp2019-07-26.[31] 史晓雨, 冀倩倩, 尚明生. 一种不完备专利自动标引方法.&nbspCN:&nbspCN109726299A,&nbsp2019-05-07.[32] 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.&nbspCN:&nbspCN109491760A,&nbsp2019-03-19.[33] 尚明生, 李锴, 张航. 一种微博转发量预测方法.&nbspCN:&nbspCN105550275B,&nbsp2019-02-26.[34] 张帆, 张学睿, 王国胤, 尚明生. 一种海量动态数据管理方法.&nbspCN:&nbspCN105426506B,&nbsp2018-10-02.[35] 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.&nbspCN:&nbspCN108595267A,&nbsp2018-09-28.[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.&nbspCN:&nbspCN106778859A,&nbsp2017-05-31.[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.&nbspCN:&nbspCN106648890A,&nbsp2017-05-10.[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.&nbspCN:&nbspCN105631465A,&nbsp2016-06-01.[39] 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.&nbspCN:&nbspCN105404678A,&nbsp2016-03-16.[40] 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.&nbspCN:&nbspCN105224504A,&nbsp2016-01-06.
出版信息
发表论文
[1] Liu, Mei, Chen, Liangming, Du, Xiaohao, Jin, Long, Shang, Mingsheng. Activated Gradients for Deep Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. 2021, http://dx.doi.org/10.1109/TNNLS.2021.3106044.[2] 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, [3] 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.[4] 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):&nbsp463-476, http://dx.doi.org/10.1109/TNSE.2020.3040407.[5] 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):&nbsp610-620, http://dx.doi.org/10.1109/TSMC.2018.2875452.[6] 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.[7] 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, [8] 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):&nbsp227-240, http://dx.doi.org/10.1109/TBDATA.2019.2916868.[9] 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, [10] 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):&nbsp5936-5948, [11] 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):&nbsp3522-3532, http://dx.doi.org/10.1109/TSMC.2019.2930525.[12] 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):&nbsp784-794, [13] 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):&nbsp420-431, [14] 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):&nbsp1096-1106, [15] 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, [16] 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):&nbsp2525-2538, [17] 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.[18] 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):&nbsp1844-1855, http://dx.doi.org/10.1109/TCYB.2019.2894283.[19] 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):&nbsp131-141, http://lib.cqvip.com/Qikan/Article/Detail?id=90687266504849574849484949.[20] 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):&nbsp793-805, [21] 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.[22] 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.[23] 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, [24] 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):&nbsp14-24, http://dx.doi.org/10.1016/j.hal.2019.01.005.[25] 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.[26] 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.[27] 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.[28] 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.[29] 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.[30] 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):&nbsphttps://doaj.org/article/86d5d46f478d40fdba436bd5559ac4c9.[31] 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.[32] 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):&nbsp2011-2022, http://119.78.100.138/handle/2HOD01W0/8021.[33] 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):&nbsp909-920, http://dx.doi.org/10.1109/TII.2017.2737827.[34] 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.[35] 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.[36] Luo Xin, Shang MingSheng. Symmetric non-negative latent factor models for undirected large networks. 26th International Joint Conference on Artificial Intelligence, IJCAI 2017null. 2017, 2435-2442, http://www.chinair.org.cn/handle/1471x/1660739.[37] 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.[38] 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):&nbsp19044-19056, https://www.webofscience.com/wos/woscc/full-record/WOS:000407723100027.[39] 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):&nbsp909-920, [40] 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.[41] 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):&nbsp61-66+3, http://lib.cqvip.com/Qikan/Article/Detail?id=668612200.[42] 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, [43] 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.[44] 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):&nbsphttps://doaj.org/article/526ebd8abab244ed9b72164ddbe8895e.[45] Guan, Yuan, Cai, Shimin, Shang, Mingsheng. Recommendation algorithm based on item quality and user rating preferences. FRONTIERS OF COMPUTER SCIENCE[J]. 2014, 8(2):&nbsp289-297, https://www.webofscience.com/wos/woscc/full-record/WOS:000334183200011.[46] 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):&nbsp3417-3423, http://dx.doi.org/10.1016/j.physa.2013.03.045.[47] Zhang, QianMing, Zeng, An, Shang, MingSheng. Extracting the Information Backbone in Online System. PLOS ONE[J]. 2013, 8(5):&nbsphttps://doaj.org/article/351df289b2f74e4cb5295e5abf8be63e.[48] 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):&nbsphttps://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 ）&nbsp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 ）&nbsp中科院****, 主持, 部委级, 2016-01--2018-12（ 3 ）&nbsp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 ）&nbsp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 ）&nbsp洛丁智慧照明系统开发, 主持, 院级, 2016-01--2025-09（ 6 ）&nbsp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 ）&nbsp大数据基础科研与开发平台建设, 主持, 省级, 2016-04--2016-12（ 8 ）&nbsp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 ）&nbsp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 ）&nbsp慢病创新服务研发及慢病示范应用, 参与, 院级, 2017-09--2018-12（ 11 ）&nbsp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 ）&nbsp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 ）&nbsp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 ）&nbsp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 ）&nbsp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 ）&nbsp监控检测技术研究, 主持, 国家级, 2019-02--2021-01（ 17 ）&nbsp数字城市大数据联合实验室, 主持, 院级, 2019-10--2022-10（ 18 ）&nbsp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 ）&nbsp新药研发大数据平台, 参与, 国家级, 2020-07--2022-06（ 20 ）&nbsp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
2013 中国科学院大学，网络信息中心.