林元国，男，厦门大学工学博士，副教授

福建省高层次人才（C类）、厦门市高层次人才、集美大学青年拔尖人才

电子邮箱：xdlyg@jmu.edu.cn

本人研究方向为大数据挖掘、可解释人工智能、推荐算法和网络信息安全。在国内外期刊和国际顶级会议发表论文20余篇。主持省、校级科研项目各1项（均已结题），作为核心成员参与国家自然科学基金面上项目、教育部社科基金以及省市级等多项科研课题。近三年以第一作者申请7项国家发明专利，其中已授权的有2项。

近年来以第一作者/通讯作者发表的代表性学术论文

1.Yuanguo Lin, Shibo Feng, Fan Lin, Wenhua Zeng, Yong Liu, Pengcheng Wu. Adaptive course recommendation in MOOCs. Knowledge-Based Systems, Volume 224, 107085, July 2021, (SCI一区期刊, CCF C).

2.Yuanguo Lin, Fan Lin, Lvqing Yang, Wenhua Zeng, Yong Liu, Pengcheng Wu. Context-aware reinforcement learning for course recommendation. Applied Soft Computing, Volume 125, 109189, August 2022, (SCI一区期刊).

3.Yuanguo Lin, Fan Lin, Wenhua Zeng, Jianbing Xiahou, Li Li, Pengcheng Wu, YongLiu, Chunyan Miao. Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation. Knowledge-Based Systems, Volume 244, 108546, May 2022, (SCI一区期刊, CCF C).

4.Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao. A Survey on Reinforcement Learning for Recommender Systems. IEEE Transactions on Neural Networks and Learning Systems, June 2023: 1-21. (SCI一区期刊, CCF B).

5.Yuanguo Lin, Shibo Feng, Fan Lin, Jianbing Xiahou, Wenhua Zeng. Multi-scale reinforced profile for personalized recommendation with deep neural networks in MOOCs. Applied Soft Computing, Volume 148, 110905, November 2023, (SCI一区期刊).

6.Yuanguo Lin, Wei Zhang, Fan Lin, Wenhua Zeng, Xiuze Zhou, Pengcheng Wu. Knowledge-aware reasoning with self-supervised reinforcement learning for explainable recommendation in MOOCs. Neural Computing and Applications, 2023, (SCI二区期刊, CCF C).

7.Wei Zhang, Yuanguo Lin（共同一作）, Yong Liu, Huanyu You, Pengcheng Wu, Fan Lin, Xiuze Zhou. Self-Supervised Reinforcement Learning with dual-reward for knowledge-aware recommendation. Applied Soft Computing, Volume 131, 109745, December 2022, (SCI一区期刊).

8.Fan Lin, Pengfei Li, Yuanguo Lin*（通讯作者）, Zhennan Chen, Huanyu You, Shibo Feng. Kernel-based Hybrid Interpretable Transformer for High-frequency Stock Movement Prediction, IEEE International Conference on Data Mining (ICDM), 2022: 241-250, (数据挖掘顶级会议, CCF B).