Ø代表性论文

(1)Jinli Li, Ye Yuan（袁野）, and Xin Luo*. Learning Error Refinement in Stochastic Gradient Descent-based Latent Factor Analysis via Diversified PID Controllers, IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2025.3547854, 2025. IF=5.3，中科院JCR分区三区

(2)Ye Yuan（袁野）,Ying Wang, and Xin Luo*, A Node-Collaboration-Informed Graph Convolutional Network for Highly Accurate Representation to Undirected Weighted Graph, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2024.3514652, 2024. IF=10.4，中科院JCR分区一区

(3)Ye Yuan（袁野）, Siyang Lu*, and Xin Luo*. A Proportional Integral Controller-Enhanced Non-negative Latent Factor Analysis Model, IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/JAS.2024.125055, 2024. IF=15.3，中科院JCR分区一区

(4)Ling Wang, Kechen Liu, Ye Yuan*（袁野）. GT-A2T: Graph Tensor Alliance Attention Network, IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/JAS.2024.124863, 2024. IF=15.3，中科院JCR分区一区

(5)Ye Yuan（袁野）#, Jinli Li#, and Xin Luo*. A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis. IEEE Transactions on Fuzzy Systems, 2024, 32(7): 4049-4061. IF=10.7，中科院JCR分区一区

(6)Ye Yuan（袁野）, Xin Luo*, and MengChu Zhou. Adaptive Divergence-based Non-negative Latent Factor Analysis of High-Dimensional and Incomplete Matrices from Industrial Applications. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(2): 1209-1222. IF=5.3，中科院JCR分区三区

(7)Jiufang Chen, Ye Yuan*（袁野）, and Xin Luo*, SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks, IEEE/CAA Journal of Automatica Sinica, 2024, 11(7): 1717-1719. IF=15.3，中科院JCR分区一区

(8)Xin Luo*, Jiufang Chen, Ye Yuan（袁野）, and Zidong Wang. Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis. IEEE Transactions on Systems Man Cybernetics: Systems, 2024, 54(4): 2213-2226. IF=8.6，中科院JCR分区一区

(9)Jinli Li, Xin Luo*, Ye Yuan（袁野）, and Shangce Gao. A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis. IEEE Transactions on Automation Science and Engineering, 2024, 21(3): 3742-3756. IF=5.9，中科院JCR二区

(10)Jiufang Chen, Kechen Liu, Xin Luo*, Ye Yuan（袁野）, Khaled Sedraoui, Yusuf Al-Turki, and MengChu Zhou*. A State-migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data, IEEE/CAA Journal of Automatica Sinica, 2024, 11(11): 2220-2235. IF=15.3，中科院JCR分区一区

(11)Ye Yuan（袁野）, Xin Luo*, Mingsheng Shang, and Zidong Wang. A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data, IEEE Transactions on Cybernetics, 2023, 53(9): 5788-5801. IF=9.4，中科院JCR分区一区

(12)Ye Yuan（袁野）, Renfang Wang*, Guangxiao Yuan, and Xin Luo. An Adaptive Divergence-based Non-negative Latent Factor Model. IEEE Transactions on System Man Cybernetics: Systems, 2023, 53(10): 6475-6487. IF=8.6，中科院JCR分区一区

(13)Ye Yuan#（袁野）, Qiang He#, Xin Luo#,*, and Mingsheng Shang*. A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices, IEEE Transactions on Big Data, 2022, 8(3): 784-794. IF=7.5，中科院JCR分区三区，ESI高引

(14)Xin Luo, Ye Yuan（袁野）, Sili Chen, Nianyin Zeng, and Zidong Wang. Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis, IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3958-3970. IF=8.9，中科院JCR分区二区，ESI高引

(15)Mingsheng Shang, Ye Yuan（袁野）, Xin Luo*, and Mengchu Zhou. An α-β-divergence-generalized Recommender for Highly-accurate Predictions of Missing User Preferences, IEEE Transactions on Cybernetics, 2022, 52(8): 8006-8018. IF=9.4，中科院JCR分区一区

(16)Xin Luo#, Ye Yuan#（袁野）, MengChu Zhou*, Zhigang Liu, and Mingsheng Shang*. Non-negative Latent Factor Model based on β-divergence for Recommender Systems. IEEE Transactions on System Man Cybernetics: Systems, 2021, 51(8): 4612-4623. IF=8.6，中科院JCR分区一区

(17)Ye Yuan（袁野）, Xin Luo*, Mingsheng Shang, and Di Wu. A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems. The Web Conference, 2020, 498-507. CCF-A类会议

(18)Ye Yuan（袁野）, Mingsheng Shang, and Xin Luo*. Temporal Web Service QoS Prediction via Kalman Filter-Incorporated Dynamic Latent Factor Analysis. European Conference on Artificial Intelligence, 2020, 561-568. CCF-B类会议

(19)Jiufang Chen#, Ye Yuan#（袁野）, Tao Ruan#, Jia Chen, and Xin Luo*. Hyper-Parameter-Evolutionary Latent Factor Analysis for High-Dimensional and Sparse Data from Recommender Systems. Neurocomputing, 2020, 421: 316-328. IF=5.5，中科院JCR分区二区

(20)Jinli Li, Ye Yuan（袁野）, Tao Ruan, Jia Chen, and Xin Luo*. A Proportional-Integral-Derivative-Incorporated Stochastic Gradient Descent-Based Latent Factor Analysis Model. Neurocomputing, 2020, 427: 29-39. IF=5.5，中科院JCR分区二区

(21)Ye Yuan（袁野）, Xin Luo*, and Mingsheng Shang. Effects of Preprocessing and Training Biases in Latent Factor Models for Recommender Systems. Neurocomputing, 2018, 275: 2019-2030. IF=5.5，中科院JCR分区二区

(22)Di Wu#, Xin Luo#, Guoyin Wang*, Mingsheng Shang*, Ye Yuan（袁野） and Huyong Yan. A Highly-Accurate Framework for Self-Labeled Semi-Supervised Classification in Industrial Applications, IEEE Transactions on Industrial Informatics, 2018, 14(3):909-920. IF=11.7，中科院JCR分区一区

Ø主持和参与科研项目

(1)国家自然科学基金面上项目，面向新型配电网的异质图表示学习方法及应用研究，2024.01-2027.12，50万，主持

(2)国家自然科学基金青年项目，基于用户动态兴趣的参数自适应推荐模型研究，2021.01-2023.12，24万，主持

(3)国家重点研发计划项目，典型场景下企业知识产权安全与合规风险智能化评估技术研究子课题（(2024YFF0908204-2），2025.04-2028.03，16万，主持

(4)JKW创新项目，嵌入式XXXX检测技术，2020.12-2021.12，80万，主持

(5)重庆市自然科学基金面上项目，基于用户动态兴趣的长效推荐模型研究，2022.08—2025.07，10万，主持

(6)国家电网公司总部科技项目，全球煤油气电耦合下我国能源安全风险识别与战略路径优化技术研究，2023.01-2024.12，40万，主持（课题2负责人）

(7)国家重点研发计划，贿赂犯罪社会关系网络的多粒度分析技术研究（课题），2017.07-2020.06，724万，参与（课题秘书）

(8)国家自然科学基金重点项目，CRISPR-Cas13-RNA复合水凝胶液滴微流体的EVs亚群RNA检测关键技术及在脓毒症细胞因子风暴预警中的应用，2021.01-2025.12，297万，参与

(9)国家自然科学基金面上项目，基于隐特征分析的信息推荐技术研究，2018.01-2021.12，66万，参与

(10)国家自然科学基金面上项目，面向海绵城市运维大数据的高维稀疏张量分析方法研究，2021.01-2024.12，56万，参与

(11)国家自然科学基金重大培育项目，面向高维稀疏时变数据的宏观趋势预测研究，2017.01-2019.12，43万，参与

Ø英文专著

(1)Ye Yuan（袁野）, Xin Luo. Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach, Springer, 978-981-19-6703-0, 2022

Ø国家发明专利

(1)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，201710930280.X，授权

(2)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，201710895442.0，授权

(3)袁野、罗辛、吴昊，一种基于广义动量的产品智能推荐装置和方法，202011042490.3，受理

(4)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，20201102649.7，授权

(5)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，202011012586.5，授权

(6)杨大堂、袁野、罗辛，基于自增强图神经网络的电网设备异常检测装置和方法，2025100424914，受理