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团队简介

智能推荐技术团队(IRT)依托上海大学计算机学院及上海市高水平地方高校重点创新团队,主要负责研究推荐算法及其应用,包括但不限于:

1)基于用户会话的序列化推荐算法;2)用户意图与偏好表征学习及差异化建模;3)推荐公平性研究;4)基于预训练大模型的生成式推荐算法;5)面向医疗等领域的推荐算法应用。

智能推荐技术团队是一个比较年轻、有活力的团队,目前拥有7名硕士生,本科生20多名。团队负责人朱能军博士是上海大学计算机学院讲师、硕导;担任CCF协同计算专委执委、YOCSEF上海学术秘书等学术职务;曾在美国罗格斯大学、百度研究院访问交流;在TOIS、TKDD、SIGIR、WSDM、IJCAI、ICDM等高水平期刊和会议上发表论文40篇左右;在CCSCW22和CCSCW23连续获得最佳论文;主持国自然青年、上海市启明星计划、上大青年英才启航计划等项目,参与国家重点研发计划、国自然等项目多项;主导设计和研发的医疗智能决策辅助系统和乳腺癌病例数据库目前服务于瑞金等40多家医院和中心。朱能军老师也是上海市一流本科课程团队成员。

团队欢迎有志于从事这方面研究的同学加入!欢迎联系朱能军老师。

成果列表

近些年,团队在重要国际期刊(包括TOIS、TKDE、TKDD、TOMM、MLJ等)和重要国际会议(如SIGIR、IJCAI、WSDM、ICDM等)发表多篇论文,部分论文如下:


书籍著作
《数据结构——C++实现(第三版)》沈俊、李晓强、朱能军、张景峤、郑宇编著,ISBN 978-7-03-077312-8
期刊
Nengjun Zhu, Jieyun Huang, Jian Cao, Liang Hu, and Siji Zhu. "Toward Medical Test Recommendation from Optimal Attribute Selection Perspectives: A Backward Reasoning Approach". Complex & Intelligent Systems 2024, Springer. (二区, Q1, Accepted)
Qiqi Cai, Jian Cao, and Guandong Xu, Nengjun Zhu. "Distributed Recommendation Systems: Survey and Research Directions". TOIS 2024, (CCF-A, Accepted)
Zixuan Yuan, Junmin Liu, Haoyi Zhou, Denghui Zhang, Hao Liu, Nengjun Zhu, and Hui Xiong. "LEVER: Online Adaptive Sequence Learning Framework for High-Frequency Trading". TKDE 2023, (CCF-A, Accepted)
Xinzhi Wang, Nengjun Zhu*, Jiahao Li, Yudong Chang, and Zhennan Li. "Entity Recognition Based on Heterogeneous Graph Reasoning of Visual Region and Text Candidate". Machine Learning, 2023, Springer. 113(8):1-20 (CCF-B, Q1)
Xiao Wei, Chenyang Huang, and Nengjun Zhu*. "Event Causality Extraction through External Event Knowledge Learning and Polyhedral Word Embedding". Machine Learning, 2023, Springer. 113(8): 5351-5378 (CCF-B, Q1)
Bohan Jia, Jian Cao, Shiyou Qian, Nengjun Zhu, Xin Dong, Liang Zhang, Lei Cheng, and Linjian Mo. "SMONE: A Session-based Recommendation Model based on Neighbor Sessions with Similar Probabilistic Intentions." ACM Transactions on Knowledge Discovery from Data (TKDD). vol. 17, no. 8, Article 111, 2023, (CCF-B)
Nengjun Zhu, Jian Cao, Xinjiang Lu, Chuanren Liu, Hao Liu, Yanyan Li, Xiangfeng Luo, Hui Xiong. "Predicting a Person’s Next Activity Region with a Dynamic Region-Relation-Aware Graph Neural Network." ACM Transactions on Knowledge Discovery from Data (TKDD). 2022. vol. 16, no. 2, 116: 1-23, (CCF-B)
Nengjun Zhu, Jian Cao, Xinjiang Lu, Hui Xiong. "Learning a Hierarchical Intent Model for Next-Item Recommendation." ACM Transactions on Information Systems (TOIS). 2021. vol. 40, no. 2, article 38, pp. 1-28, (CCF-A)
Nengjun Zhu, Jian Cao, Xinjiang Lu, Qi Gu. "Leveraging pointwise prediction with learning to rank for top-N recommendation." World Wide Web. Springer US, 2020. vol. 24, no. 1, pp. 375-396. (CCF-B)
Nengjun Zhu, Jian Cao, Kunwei Shen, Xiaosong Chen, and Siji Zhu. "A Decision Support System with Intelligent Recommendation for Multi-Disciplinary Medical Treatment." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). ACM, 2020. vol. 16, no. 1s, 33, pp1-23 (CCF-B)
会议
Xinzhi Wang, Mengyue Li, Nengjun Zhu*, Jiayan Qian, and Zhanyi Zheng. "Early Fire Detection based on Local Morphological Knowledge Matching". in Proceedings of IEEE ICDM 2024 (CCF-B, Accepted)
Nengjun Zhu, Lingdan Sun, Xiangfeng Luo, Jian Cao, Qi Zhang, and Xinjiang Lu. "Exploitation or Exploration Next? User Behavior Decoupling and Emerging Intent Modeling for Next-Item Recommendation". in Proceedings of IEEE ICDM 2024 (CCF-B, Accepted)
Shouyu Chen, Tangwei Ye, Laizhong Yuan, Qi Zhang, Ke Liu, Usman Naseem, Ke Sun, Nengjun Zhu, and Liang Hu. “VR-DiagNet: Medical Volumetric and Radiomic Diagnosis Networks with Interpretable Clinician-like Optimizing Visual Inspection.” in Proceedings of ACM MM 2024 (CCF-A, Accepted)
Nengjun Zhu, Jieyun Huang, Jian Cao, Liang Hu, Zixuan Yuan, and Huanjing Gao. "R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-channel Information Fusion for Therapy Recommendation." in Proceedings of IJCAI 2024, pp 2634-2641 (CCF-A)
Nengjun Zhu, Jieyun Huang, Jian Cao, Xinjiang Lu, Hao Liu, and Hui Xiong. "MtiRec: A Medical Test Recommender System based on the Analysis of Treatment Programs." in Proceedings of the IEEE ICDM 2023, pp. 898-907. (Regular Paper, CCF-B).
Nengjun Zhu, Jieyun Huang, Jian Cao, and Shanshan Feng. "Learning User Embeddings based on Long Short-Term User Group Modeling for Next-Item Recommendation." in Proceedings of CCF Conference on Computer Supported Cooperative Work and Social Computing. Springer, 2022.11 (最佳论文奖)
Haoran Xin, Xinjiang Lu, Nengjun Zhu, Tong Xu, Dejing Dou, Hui Xiong "CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users." in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR). ACM, 2022. (CCF-A)
Nengjun Zhu, Jian Cao, Yanchi Liu, Yang Yang, Haochao Ying, and Hui Xiong. "Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation." in Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM). ACM, 2020. pp807-815 (CCF-B, Research paper)
合作单位