教学和研究领域

教学课程：人工智能与经管研究（硕博研究生），人工智能（本科，计划开设）

研究领域：从事机器学习算法及信息系统设计科学相关研究，具体关注个性化推荐系统、大语言模型、可信人工智能等领域

学术经历

2025年7月-至今 特聘副研究员，管理科学与工程系，AV淘宝

2022年11月-2023年6月 访问学者，金融服务分析研究院，美国特拉华大学

企业实践经历

2025年7月-至今 顾问，大模型相关项目，美团

获奖与荣誉

国际信息系统年会（ICIS）Best Student Paper Nominee，2024

编审经历

期刊：Information & Management，Scientific Data

会议：ICIS，PACIS，WITS，ICDM，PAKDD

主要论文及书籍

国际期刊论文

Shangkun Che, Hongyan Liu*, and Shen Liu (2024). Tagging Items with Emerging Tags: A Neural Topic Model based Few-Shot Learning Approach. ACM Transactions on Information Systems (TOIS).

Xiao Fang*, Shangkun Che*, Minjia Mao, Hongzhe Zhang, Ming Zhao, and Xiaohang Zhao (2024). Bias of AI-generated content: an examination of news produced by large language models. Scientific Reports. (ESI高被引论文)

Yi Bu, Binglu Wang, Win-Bin Huang*, Shangkun Che, Yong Huang (2018). Using the appearance of citations in full text on author co-citation analysis. Scientometrics.国内期刊论文

韩雪雯，车尚锟，杨梦晴*，王能 (2024). 多模态数据驱动的AI智能体模式设计. 图书情报工作

王潇, 刘红岩*, 车尚锟 (2021). 一种基于深度强化学习的直播推荐方法. 信息系统学报

王越千, 黄文彬, 步一*, 车尚锟 (2021). 学术论文子句语义类型自动标注技术研究. 情报学报.

黄文彬, 车尚锟* (2019). 计算文本相似度的方法体系与应用分析. 情报理论与实践.

尚闻一*, 车尚锟 (2019). 群体极化还是协商调和？--维基百科Islamophobias词条实证研究. 图书馆论坛.

会议论文

Shangkun Che, Minjia Mao, Hongyan Liu. New Community Cold-Start Recommendation: A Novel Large Language Model-based Method. International Conference on Information Systems. (ICIS, 2024)

Xuewen Han, Neng Wang, Shangkun Che, Hongyang Yang, Kunpeng Zhang, Sean Xin Xu. Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research. ACM International Conference on AI in Finance (ICAIF, 2024)

Shangkun Che, Hongyan Liu, Xiaojie Mao, Silin Du. Everything Has Its Price: The Fairness Cost of Fine-tuning Large Language Models for Recommendation. China Summer Workshop on AI in Business. (SWAIB, 2024)

Shen Liu, Shangkun Che, Hongyan Liu*. Enhancing Recommendation Interpretability with Tags: A Neural Variational Model. International Conference on Information Systems. (ICIS, 2022)

Shangkun Che, Hongyan Liu*, Xiaojie Mao. Measuring Counterfactual Fairness of Recommendation Systems: An Identifiable Causal Model. 31^st Workshop on Information Technologies and Systems. (WITS, 2021)

Shangkun Che, Hongyan Liu*, Xiaojie Mao. Counterfactual Fairness for Recommendation System: Model, Identification and Measurement. The 5^th INFORMS Workshop on Data Science. (Informs DS, 2021)

Yi Bu, Binglu Wang, Win-Bin Huang*, Shangkun Che. MFTACA：An Author Co-citation Analysis Method Combined with Metadata in Full Text. The International Conference on Scientometrics & Informatics. (ISSI, 2017)

专利

刘红岩, 高歌, 车尚锟, 杜思霖, 景昊, 谢志辉, 吴显仁, 徐伟招 (2024). 一种基于招聘需求相似度的职位推荐方法. 国家发明专利.

刘红岩, 车尚锟, 王潇 (2021). 模型训练方法、直播推荐方法、设备、程序产品. 国家发明专利.