About Me

Hi! My name is Du Yingpeng. I am a research fellow at Nanyang Technological University’s College of Computing and Data Science, mentored by Prof. Zhang Jie. Before that, I obtained my Ph.D. degree in Software Engineering at Peking University (PKU), in July 2023, where I am advised by Prof. Liu Hongzhi and Prof. Wu Zhonghai. I received my M.S. degree in Computer Technology at Peking University (PKU), in July 2019, where I am advised by Prof. Liu Hongzhi. I received my B.E. degree from School of Mathematics, Sichuan University (SCU), in June 2016. I have published more than 20 papers in top-tier journals and conferences, such as TPAMI, JMLR, Pattern Recognition, AAAI, SIGKDD, ICDM, IJCAI, and WWW.

Welcome to see my publications, academic services, experience, and honors & awards, and welcome to reach out for collaboration! You may approach me at:

E-mail: dyp1993 [at] pku [dot] edu [dot] cn

💡 Research Interests

My research focuses on recommender systems and ensemble learning (AI and data mining in computer science). I am interested in the problems of utilizing different kinds of information, e.g. sequential information and side information, and models to enhance the accuracy and stability of systems. Recently, I am interested in utilizing large language models to improve the effectiveness and efficiency of recommender systems. To this end, I mainly study the following topics: ambiguity decomposition theory, diversity learning, sequential pattern mining, graph representation, causal learning, reinforcement learning, large language model-based recommendation, etc.

Research Keywords

Recommender systems：General recommendation, Sequential recommendation, context-aware recommendation, social recommendation, attribute-based recommendation, job recommendation, graph-based recommendation and etc.

LLM-based recommendation: Effectiveness and efficiency issues in recomemnder systems, e.g., self-reflection, text enhancement, knowledge distillation, and etc.

Ensemble learning: Ambiguity decomposition theory, Ranking ensemble, Sequential ensemble, and etc.