Dr. Li Chen is currently an Associate Professor in the Department of Computer Science at Hong Kong Baptist University. She obtained her PhD degree in Computer Science at Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and Bachelor and Master Degrees in Computer Science at Peking University, China. Her research interests are mainly in the areas of human-computer interaction, intelligent Web technologies, recommender systems, and artificial intelligence. She has authored and co-authored more than 80 publications that appear in journals (such as IJHCS, TOCHI, TIST, KNOSYS, UMUAI, AI Magazine, ECRJ) and leading conferences in the areas of e-commerce, artificial intelligence, intelligent user interfaces, user modeling, and recommender systems (e.g., IJCAI, SDM, IUI, UMAP, ACM RecSys, ACM EC, AAAI). She is now an ACM senior member, and an editorial board member of User Modeling and User-Adapted Interaction Journal (UMUAI). She has also been serving in a number of journals and conferences as guest editor, co-organizer, PC member and reviewer.
- Human-Computer Interaction
- Data-Driven Web Personalization
- Recommender Systems
- Artificial Intelligence and User Interfaces
- Social Web
- Wen Wu, Li Chen, and Yu Zhao. Personalizing Recommendation Diversity based on User Personality. User Modeling and User-Adapted Interaction Journal (UMUAI), 2018. (forthcoming)
- Li Chen, Dongning Yan, and Feng Wang. User Perception of Sentiment-Integrated Critiquing in Recommender Systems. International Journal of Human-Computer Studies (IJHCS), 2017. (forthcoming)
- Li Chen, Feng Wang, and Pearl Pu. Investigating Users’ Eye Movement Behavior in Critiquing-based Recommender Systems. AI Communications, vol. 30(3-4), pages 207-222, 2017.
- Li Chen and Feng Wang. Explaining Recommendations Based on Feature Sentiments in Product Reviews. In Proceedings of 22nd ACM International Conference on Intelligent User Interfaces (IUI’17), pages 17-28, Limassol, Cyprus, March 13-16, 2017.
- Weike Pan and Li Chen. Group Bayesian Personalized Ranking with Rich Interactions for One-Class Collaborative Filtering. Neurocomputing, vol. 207, pages 501–510, September 2016.
- Guanliang Chen and Li Chen. Augmenting Service Recommender Systems by Incorporating Contextual Opinions from User Reviews. User Modeling and User-Adapted Interaction Journal (UMUAI), Special Issue on User Modeling in Ubiquitous Computing, vol. 25(3), pages 295-329, 2015.
- Li Chen, Guanliang Chen, and Feng Wang. Recommender Systems Based on User Reviews: The State of the Art. User Modeling and User-Adapted Interaction Journal (UMUAI), vol. 25(2), pages 99-154, 2015.
- Li Chen and Pearl Pu. Experiments on User Experiences with Recommender Interfaces. Behaviour & Information Technology, vol. 33(4), pages 372-394, 2014.
- Li Chen and Feng Wang. Preference-based Clustering Reviews for Augmenting e-Commerce Recommendation. Knowledge-Based Systems (KNOSYS), vol. 50, pages 44-59, 2013.
- Weike Pan and Li Chen. GBPR: Group Preference based Bayesian Personalized Ranking for One-Class Collaborative Filtering. In Proceedings of 23rd International Joint Conference on Artificial Intelligence (IJCAI’13), pages 2691-2697, Beijing, China, August 3-9, 2013.
- Weishi Zhang, Guiguang Ding, Li Chen, Chunping Li and Chengbo Zhang. Generating Virtual Ratings from Chinese Reviews to Fuse into Collaborating Filtering Algorithms. ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4(1), 2013.