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Associate Head (RSH) and Associate Professor, Department of Computer Science Associate Professor (Affiliate), Department of Social Work Associate Program Director, Master of Science in AI and Digital Media


Dr. Li Chen is currently an Associate Professor in the Department of Computer Science at Hong Kong Baptist University (HKBU). She obtained her PhD degree in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and Bachelor and Master degrees from Peking University, China. Her recent research focus has mainly been on recommender system design and development, which integrates approaches of multiple disciplines including artificial intelligence, human-computer interaction, user modeling, and user behavior analytics. The application domains cover social media, e-commerce, and online education. She has authored and co-authored over 100 publications, most of which have appeared in refereed high-impact journals (e.g., IJHCS, TOCHI, UMUAI, TIST, TIIS, KNOSYS, Behavior & Information Technology, AI magazine, AI Communications, and IEEE Intelligent Systems) and conferences (e.g., WWW, IUI, CHI, CIKM, SIGKDD, SDM, IJCAI, AAAI, ACM RecSys, ACM UMAP, Interact), with over 5,000 citations so far. Her co-authored paper received UMUAI 2018 James Chen Best Paper Award. She is now an ACM senior member, steering committee member of ACM Conference on Recommender Systems (RecSys), editorial board member of User Modeling and User-Adapted Interaction Journal (UMUAI), editorial board member of Journal of Intelligent Information Systems (JIIS), and associate editor of ACM Transactions on Interactive Intelligent Systems (TiiS). She has also served as program co-chair of ACM RecSys'20 and ACM UMAP'18.


Project Highlights


Overview of our proposed Neural Template (NETE) explanation generation framework that consists of two basic modules respectively for rating prediction (left) and explanation generation (right).
The structure of our proposed GFRU decoder with three components.


The interaction between users and dialogue-based recommender systems and our research focuses (i.e., user intent and satisfaction prediction).


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