基本信息
史晓雨 男 硕导 中国科学院重庆绿色智能技术研究院
电子邮件: xiaoyushi@cigit.ac.cn
通信地址: 重庆市北碚区方正大道266号
个人学术主页:https://shixiaoyu0216.github.io/
研究领域
- Trustway AI technologies: Fairness, Robustness, and O.O.D Generalization
- Data Science, Data Mining && Machine Learning: Methods and Applications
- Recommender Systems && Cloud-Edge Resource Management
招生信息
**欢迎执着创新、积极主动、志存高远而又能脚踏实地的本科生和研究生同学加入团队
招生专业
081203-计算机应用技术
招生方向
可信任机器学习大数据挖掘推荐系统,云-边计算
教育背景
2012-09--2014-06 美国田纳西大学电子工程与计算机科学系 联合培养博士(国家公派)2008-09--2015-06 电子科技大学计算机学院 工学博士2003-09--2007-06 解放军信息工程大学电子技术学院 本科学士
工作经历
工作简历
2018-01~现在, 中国科学院重庆绿色智能技术研究院, 副研究员2015-07~2017-12,中国科学院重庆绿色智能技术研究院, 助理研究员
专利与奖励
奖励信息
(1) 猪八戒网众创平台智能服务关键技术及应用, 一等奖, 省级, 2021
专利成果
( 1 ) 一种融合用户偏好预测的深度强化学习推荐方法, 发明专利, 2021, 第 1 作者, 专利号: CN202111519219.9( 2 ) 基于邻域粗糙集和PCA融合的数据分类预测方法, 专利授权, 2021, 第 7 作者, 专利号: CN107016416B( 3 ) 一种对抗攻击敏感的文本分类方法, 发明专利, 2020, 第 1 作者, 专利号: CN111984762A( 4 ) 一种混凝土生产配合比的智能设计方法, 专利授权, 2020, 第 1 作者, 专利号: CN110435009B( 5 ) 一种面向不平衡文本数据的自分类方法, 发明专利, 2019, 第 1 作者, 专利号: CN110609898A( 6 ) 一种基于用户自主选择的个性化推荐方法和系统, 专利授权, 2019, 第 2 作者, 专利号: CN105512183B( 7 ) 一种混凝土28d抗压强度预测方法, 发明专利, 2019, 第 1 作者, 专利号: CN110263431A( 8 ) 一种基于用户优先度的遥感分发方法及系统, 专利授权, 2019, 第 12 作者, 专利号: CN107104956B( 9 ) 一种不完备专利自动标引方法, 发明专利, 2019, 第 1 作者, 专利号: CN109726299A( 10 ) 一种高效能数据中心云服务器资源自主管理方法和系统, 发明专利, 2019, 第 1 作者, 专利号: CN109491760A( 11 ) 一种基于深度强化学习的资源调度方法和系统, 发明专利, 2018, 第 6 作者, 专利号: CN108595267A( 12 ) 一种能耗感知的云计算服务器资源在线管理方法和系统, 发明专利, 2017, 第 1 作者, 专利号: CN106648890A( 13 ) 一种在线系统中用户定制推荐系统的方法, 发明专利, 2016, 第 3 作者, 专利号: CN105404678A
出版信息
发表论文
(1) Maximum Entropy Policy for Long-term Fairness in Interactive Recommender Systems, IEEE Transactions on Services Computing, 2024, 第 1 作者(2) Hierarchical Reinforcement Learning for Long-term Fairness in Interactive Recommendation, IEEE International Conference on Web Services (ICWS 2024), 2024, 第 2 作者 通讯作者(3) RTiSR: a review-driven time interval-aware sequential recommendation method., Journal of Big Data, 2023, 第 1 作者(4) Towards Long-term Fairness in Interactive Recommendation: A Maximum Entropy Reinforcement Learning Approach, IEEE International Conference on Web Services (ICWS 2023), 2023, 第 1 作者(5) Neighbor Importance-aware Graph Collaborative Filtering for Item Recommendation, Neurocomputing, 2023, 第 11 作者(6) A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads, International Conference on Database Systems for Advanced Applications, 2023, 第 11 作者(7) Graph neural networks via contrast between separation and aggregation for self and neighborhood, EXPERT SYSTEMS WITH APPLICATIONS, 2023, 第 11 作者(8) Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach, ACM Transactions on Information Systems (TOIS), 2023, 第 1 作者(9) Performance and cost-aware task scheduling via deep reinforcement learning in cloud environment, International Conference on Service-Oriented Computing, 2022, (10) Performance and Cost-Aware Task Scheduling via Deep Reinforcement Learning in Cloud Environment, Performance and Cost-Aware Task Scheduling via Deep Reinforcement Learning in Cloud Environment, 2022, 第 11 作者(11) Aspect-aware Asymmetric Representation Learning Network for Review-based Recommendation, International Joint Conference on Neural Networks, 2022, 第 11 作者(12) Large-Scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems, IEEE TRANSACTIONS ON BIG DATA, 2022, 第 1 作者(13) Siamese Generative Adversarial Predicting Network for Extremely Sparse Data in Recommendation System, 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, 2021, 第 11 作者(14) Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation, ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2021, PT II, 2021, 第 11 作者(15) Dynamical Conventional Neural Network Channel Pruning by Genetic Wavelet Channel Search for Image Classification, FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2021, 第 11 作者(16) Deep Sentiment Learning Network for Temporal-aware Recommendation Based on User Reviews, 2021 IEEE 6TH INTERNATIONAL CONFERENCE ON BIG DATA ANALYTICS (ICBDA 2021), 2021, 第 11 作者(17) SLAs-aware Online Task Scheduling based on Deep Reinforcement Learning Method in Cloud Environment, 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems : Volume 3 of 4., 2020, 第 11 作者(18) seIMC: A GSW-Based Secure and Efficient Integer Matrix Computation Scheme With Implementation, IEEE ACCESS, 2020, 第 2 作者(19) Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method, 15TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2019, CO-LOCATED WITH THE 5TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS, ICHSA 2019, 2020, 第 2 作者(20) A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label, 15TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2019, CO-LOCATED WITH THE 5TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS, ICHSA 2019, 2020, 第 2 作者(21) Elastic-net regularized latent factor analysis-based models for recommender systems, NEUROCOMPUTING, 2019, 第 4 作者(22) A Deep Temporal Collaborative Filtering Recommendation Framework via Joint Learning from Long and Short-Term Effects, 2019 IEEE Int’l Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, (ISPA), 2019, 第 1 作者(23) Dccr: deep collaborative conjunctive recommender for rating prediction, IEEE ACCESS, 2019, 第 3 作者(24) TCR: Temporal-CNN for Reviews Based Recommendation System, 2ND INTERNATIONAL CONFERENCE ON DEEP LEARNING TECHNOLOGIES, ICDLT 2018, 2018, 第 2 作者(25) Incremental Slope-one recommenders, NEUROCOMPUTING, 2018, 第 4 作者(26) On minimizing total energy consumption in the scheduling of virtual machine reservations, JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2018, 第 5 作者(27) Long-term performance of collaborative filtering based recommenders in temporally evolving systems, NEUROCOMPUTING, 2017, 第 1 作者(28) Long-term effects of user preference-oriented recommendation method on the evolution of online system, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 第 1 作者(29) 基于Spark的标准化PCA算法, Normalized PCA Algorithm Based on Spark, 郑州大学学报:工学版, 2017, 第 4 作者(30) Water Bloom Precursor Analysis Based on Two Direction S-Rough Set, WATER RESOURCES MANAGEMENT, 2017, 第 8 作者(31) User Heterogeneity and Individualized Recommender, CHINESE PHYSICS LETTERS, 2017, 第 3 作者(32) Water eutrophication assessment based on rough set and multidimensional cloud model, CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2017, 第 7 作者(33) PAPMSC: Power-Aware Performance Management Approach for Virtualized Web Servers via Stochastic Control, JOURNAL OF GRID COMPUTING, 2016, 第 11 作者(34) Power-aware performance management of virtualized enterprise servers via robust adaptive control, CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2015, 第 11 作者(35) 基于自适应控制方法的云计算服务器性能管理, Performance Management of the Cloud Server Based on Adaptive Control Approach, 计算机与数字工程, 2014, 第 3 作者(36) 神经网络结合并行混沌粒子群优化算法的波前预测器, A Novel Neural Network Wavefront Predictor Based on Parallel Chaotic PSO Algorithm, 计算机与数字工程, 2013, 第 3 作者
科研活动
科研项目
( 1 ) 金融大模型智能服务平台关键技术研发及应用, 负责人, 地方任务, 2023-12--2026-12( 2 ) 美国药品不良反应事件数据清洗和扫图识药项目, 负责人, 境内委托项目, 2022-05--2023-08( 3 ) 中国科学院“”西部青年“项目-面向用户偏好不确定的序列推荐技术研究, 负责人, 中国科学院计划, 2022-01--2024-12( 4 ) 基于深度强化学习的长效序列推荐系统研究, 负责人, 地方任务, 2021-06--2022-05( 5 ) 新冠肺炎药物研发大数据平台关键技术研发, 负责人, 地方任务, 2020-02--2020-06( 6 ) 服务机器人云服务平台研制与应用, 负责人, 地方任务, 2020-01--2021-12( 7 ) 大数据在药物化学合成中的应用, 负责人, 地方任务, 2019-08--2021-08( 8 ) 建筑行业大数据平台建设与应用示范, 负责人, 地方任务, 2018-01--2020-06( 9 ) 中科院青年创新会人才计划, 负责人, 中国科学院计划, 2017-01--2020-12
参与会议
(1)A Deep Temporal Collaborative Filtering Recommendation Framework via Joint Learning from Long and Short-Term Effects 2019-12-18(2)SLAs-aware Online Task Scheduling based on Deep Reinforcement Learning Method in Cloud Environment 2019-08-10
指导学生
已指导学生
刘泉亮 硕士研究生 085404-计算机技术
现指导学生
夏崇珺 硕士研究生 085404-计算机技术
鲁云 硕士研究生 081202-计算机软件与理论
龚镇辉 硕士研究生 085400-电子信息
张智霖 硕士研究生 085400-电子信息