研究方向
| 研究方向： 赵勤，男，博士，副教授，硕士生导师，中国计算机学会高级会员，上海师范大学智能大数据与物联网重点实验室副主任。2016年毕业于同济大学嵌入式系统与服务计算教育部重点实验室，获工学博士学位，导师为中国工程院院士蒋昌俊教授。同年进入上海师范大学计算机系任教，2017年被评为硕士研究生导师，2019年任计算机科学与技术（师范）专业负责人，2021年入选上海市“数字人文资源建设与研究”重点创新团队，2022年挂任上海市人大财政经济委员会立法监督处副处长。主持国家重点研发计划子课题、国家自然科学基金、国家教育部重点实验室课题及上海市科委、上海市教委等多项重要科研项目，并作为主要研究人员参与国家973计划、863计划、国家基金委重大研究计划及上海市市级科技重大研究专项等多项研究工作，在IEEE TSMC、TCSS等国际高水平学术刊物上发表学术论文数十篇。 主要研究方向：社交网络分析、数据挖掘、人工智能应用等。 主要项目： 1. 国家重点研发计划专项“面向微小型数据中心的系统软件”（项目编号2022YFB4501700）子课题“数据原位计算与热敏制导存储技术”，主持，在研。 2. 国家自然科学基金青年科学基金项目“基于情感的社交网络信息推荐关键技术研究”（项目编号61702333），主持，已结题。 3. 上海市中小学在线教育研究基地项目“人工智能驱动的在线远程智能教育模式与建设路径研究”，主持，已结题。 4. 国家教育部嵌入式系统与服务计算重点实验室开放课题“大数据环境下的社交网络信息表达方法研究”（项目编号ESSCKF 2019-03），主持，已结题。 5. 国家教育部嵌入式系统与服务计算重点实验室开放课题“基于情感的混合领域社交网络信息推荐方法研究”（项目编号ESSCKF 2016-01），主持，已结题。 6. 上海高校青年教师培养资助计划项目“基于用户情感的重叠社区检测研究”，主持，已结题。 7. 上海师范大学教学改革项目“新工科视野下的信息工程数学：《线性代数》数字课程建设”，主持，已结题。 8. 国家重点基础研究发展计划（973计划）项目“信息服务的模型与机理研究”（项目编号2010CB328100），参与，已结题。 9. 国家自然科学基金重大研究计划集成项目“可信网络交易软件系统试验环境与示范应用”（项目编号91218301），参与。 10. 国家高技术研究发展计划（863计划）课题“软件安全性病态模型及其检测、防治技术研究”（2009AA01Z401），参与，已结题。 11. 上海市基础研究领域重大科技项目“大规模信息流处理计算式的基础研究” (10DJ1400300)，参与，已结题。 代表性论文： [1] Q. Zhao, C. Wang, P. Wang, M. Zhou and C. Jiang*, “A Novel Method on Information Recommendation via Hybrid Similarity”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 3, pp. 448-459, 2018. (SCI, JCR Q1, 中科院一区, TOP期刊, IF=11.5) [2] D. Qin, J. Yu, G. Zou, R. Yong, Q. Zhao*, and B. Zhang*, “A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration”, IEEE Access, vol. 7, pp. 20050-20059, 2019. (SCI，截止2023年4月被引178次) [3] Q. Zhao, J. Huang, G. Liu*, Y. Miao, and P. Wang*, “A Multi-Interest and Social Interest-Field Framework for Social Recommendations”, IEEE Transactions on Computational Social Systems, Pulished Online (DOI: 10.1109/TCSS.2023.3252611). (SCI, 中科院二区, IF=4.7) [4] Q. Zhao, G. Liu, F. Yang, R. Yang*, Z. Kou, and D. Wang, “Self-Supervised Signed Graph Attention Network for Social Recommendation”, in Proceedings of the International Joint Conference on Neural Networks, 2023, Accepted. (CCF-C) [5] Q. Zhao, Y. Miao, G. Liu, D. An, J. Lian*, and M. Li, “A Novel Self-Supervised Signed Graph Attention Method for Intelligent Decision-Making”, IEEE Transactions on Cybernetics, Submitted. (SCI, JCR Q1, 中科院一区, TOP期刊, IF=19.1) 2023年： [1] Q. Zhao, J. Huang, G. Liu*, Y. Miao, and P. Wang*, “A Multi-Interest and Social Interest-Field Framework for Social Recommendations”, IEEE Transactions on Computational Social Systems, Pulished Online (DOI: 10.1109/TCSS.2023.3252611). (SCI, 中科院二区) [2] Q. Zhao, G. Liu, F. Yang, R. Yang*, Z. Kou, and D. Wang, “Self-Supervised Signed Graph Attention Network for Social Recommendation”, in Proceedings of the International Joint Conference on Neural Networks, 2023, Accepted. (CCF-C) [3] Q. Zhao, Y. Miao, J. Huang, S. Huang, and J. Lian*, “TaneNet: Two-level Attention Network Based on Emojis for Sentiment Analysis”, IEEE Transactions on Neural Networks and Learning Systems, Submitted. (SCI, 中科院一区, TOP期刊) [4] Q. Zhao, Y. Miao, G. Liu, D. An, J. Lian*, and M. Li, “A Novel Self-Supervised Signed Graph Attention Method for Intelligent Decision-Making”, IEEE Transactions on Cybernetics, Submitted. (SCI, JCR Q1, 中科院一区, TOP期刊) 2022年： [1] Q. Zhao, Z. Zhou, J. Li, S. Jia, and J. Pan*, “Time-Dependent Prediction of Microblog Propagation Trends Based on Group Features”, Electronics, vol. 11, no. 16, pp. 2585, 2022. (SCI) [2] N. Fu, Q. Zhao*, Y. Miao, B. Zhang, and D. Wang, “A Representation Learning Method of Graph Convolutional Network Based on Structure Enhancement”, Computing and Informatics, vol. 41, no. 6, pp. 1563–1588, 2022. (SCI) [3] J. Pan, H. Li, J. Teng, Q. Zhao*, M. Li, “Dynamic Network Representation Learning Method Based on Improved GRU Network”, Computing and Informatics, vol. 41, no. 6, pp. 1491–1509, 2022. (SCI) 2021年以前： [1] Q. Zhao*, C. Wang, C. Jiang, “HSim: A Novel Method on Similarity Computation by Hybrid Measure”, in Proceedings of 2015 IEEE International Conference on Information and Communication Systems (ICICS), pp. 160-165, 2015. (EI) [2] 赵勤, 王成, 王鹏伟. 一种基于社区分类的社交网络用户推荐方法[J]. 计算机科学, 2016, 43(5): 198-203. (北大核心) [3] Q. Zhao, Y. He, C. Jiang*, P. Wang, M. Qi, M. Li, “Integration of Link and Semantic Relations for Information Discovery and Recommendation”, Computing and Informatics, vol. 35, no. 1, pp. 30-54, 2016. (SCI) [4] B. Zhang, H. Zhang, M. Li*, Q. Zhao, J. Huang, “Trust Traversal: A Trust Link Detection Scheme in Social Network”, Computer Networks, vol. 120, pp. 105-125, 2017. (SCI) [5] B. Zhang, C. Mu, Q. Zhao*, Z. Peng, J. Ding, B. Liu, “A Multidimensional Comprehensive Recommendation Method Based on Social Network”, in Proceedings of 5th International Conference on Advanced Cloud and Big Data (CBD), Shanghai, China, 2017.08.13-08.16. (EI) [6] 张波, 金玉鹏, 张倩, 赵勤, 王娇燕. 试论一种新型在线教育资源大数据组织框架, 中国电化教育, 2018 (3): 41-46. (CSSCI) [7] Q. Zhao, C. Wang, P. Wang, M. Zhou and C. Jiang*, “A Novel Method on Information Recommendation via Hybrid Similarity”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 3, pp. 448-459, 2018. (SCI, JCR Q1, 中科院一区, TOP期刊) [8] D. Qin, J. Yu, G. Zou, R. Yong, Q. Zhao*, and B. Zhang*, “A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration”, IEEE Access, vol. 7, pp.20050-20059, 2019. (SCI) [9] W. Liu, P. Wang*, Y. Meng, Q. Zhao*, C. Zhao, Z. Zhang, “A Novel Model for Optimizing Selection of Cloud Instance Types”, IEEE Access, vol. 7, pp. 120508 – 120521, 2019. (SCI) [10] 秦东明, 丁志军, 金玉鹏, 赵勤*. 基于自编码网络的空气污染物浓度预测[J]. 同济大学学报: 自然科学版, 2019, 47(5): 681-687. (EI) [11] S. Huang, Q. Zhao*, X. Xu, B. Zhang*, D. Wang, “Emojis-Based Recurrent Neural Network for Chinese Microblogs Sentiment Analysis”, in Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 59-64, November 2019. (EI) [12] 秦东明, 喻剑, 张波, 赵勤*. 基于分布式无共享架构的海量数据并行查询平台[J]. 计算机科学, 2019, 46(4): 44-49. (北大核心) [13] B. Zhang, L. Zhang, C. Mu, Q. Zhao, Q. Song, and X. Hong*, “A Most Influential Node Group Discovery Method for Influence Maximization in Social Networks: A Trust-Based Perspective”, Data & Knowledge Engineering, vol. 121, pp. 71–87, 2019. (SCI) [14] C. Wang, H. Zhu, C. Wang, Q. Zhao, and B. Zhang, “Transport Complexity of Data Dissemination in Large-Scale Online Social Networks”, in Proceedings of the ACM Turing Celebration Conference-China, pp. 1–5, 2019. (EI) [15] G. Zou, B. Zhang, R. Yong, D. Qin, and Q. Zhao, “FDN-learning: Urban PM2. 5-concentration Spatial Correlation Prediction Model Based on Fusion Deep Neural Network”, Big Data Research, vol. 26, pp. 100269, 2021. (SCI) 欢迎各年级优秀研究生和本科生加入上海师范大学智能大数据与物联网实验室！
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