Kun Zhang, Ph.D., Lecturer and Master Supervisor of Hefei University of Technology. His main research interests include research in the fields of text semantic understanding, semantic matching, semantic reasoning, text-based recommender systems, etc. He has published more than 20 papers in top conferences such as SIGIR, WWW, ACL, AAAI, and ICDM, as well as top journals including TSMC-S, TNNLS, and TKDE. He was selected for the 2021 Microsoft Research Asia Cast Star Scholars Programme, won the KDD2018 Best Student Paper Award, and served as a program committee member of international conferences such as SIGIR, ACL, AAAI, EMNLP, and so on. 

For his research, he has proposed a dynamic attention mechanism incorporating a priori knowledge of users' reading habits, which achieves dynamic attention to text focus content during semantic reasoning, significantly better than existing attention mechanism methods. The results were published in AAAI [1], the top academic conference in the field of Artificial Intelligence; He has proposed a multimodal graph modeling method incorporating user attribute information (age, occupation, etc.), which achieves the modelling of user preferences using attribute information, and better solves the cold-start problem in the recommendation system under the premise of guaranteeing the recommendation effect. The results were published in SIGIR [2], the top academic conference in the field of information retrieval.

For more information, please visit the individual academic homepage：https://zhangkunzk.github.io/