Dr. JIN, Yucheng
Dr. JIN, Yucheng

BM, MSc, PhD
Research Assistant Professor, Department of Computer Science


Dr. Yucheng Jin is a Research Assistant Professor in the Department of Computer Science, Hong Kong Baptist University. He worked as a senior UX designer in Lenovo Research from December 2016 to March 2021 and chiefly designed several influential industry chatbots such as Moli. Some of his designed products also won the design awards of iF and Red Dot. He obtained his Ph.D. degree from the University of Leuven (KU Leuven) in 2019 and the M.Sc. degree from Technical University of Munich (TUM) in 2013. He received his bachelor's degree in management from Wuhan University in 2010. His research is situated in the long-standing quest to "augment the human intellect." The current research topics include 1) improving user interaction with AI systems, such as recommender systems, smart devices, and 2) augmenting human design creativity through AI technologies such as GANs. He has published more than 20 papers in the leading conferences and journals of Human-Computer Interaction (e.g., IUI, UMAP, RecSys, AVI, UMUAI.) and held more than ten patents. Moreover, he has participated in two EU Commission-funded research projects, FP7-Serenoa (€5,100,000) and Horizon2020-TAPPS (€895,280).

Research Interests

  • Recommender Systems
  • Intelligent User Interfaces
  • Augmented Creativity
  • Interactive Media Arts

Selected Publications

  • Cai, Wanling, Yucheng Jin, and Li Chen. "Critiquing for Music Exploration in Conversational Recommender Systems." In 26th International Conference on Intelligent User Interfaces (IUI'21), pp. 480-490. 2021.
  • Jin, Yucheng, Nava Tintarev, Nyi Nyi Htun, and Katrien Verbert. Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Modeling and User-Adapted Interaction (UMUAI) 30, no. 2 (2020): 199-249.
  • Jin, Yucheng, Wanling Cai, Li Chen, Nyi Nyi Htun, and Katrien Verbert. MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM'19), pp. 951-960. 2019.
  • Jin, Yucheng, Nyi Nyi Htun, Nava Tintarev, and Katrien Verbert. Contextplay: Evaluating user control for context-aware music recommendation. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP'19), pp. 294-302. 2019.
  • Jin, Yucheng, Nava Tintarev, and Katrien Verbert. Effects of personal characteristics on music recommender systems with different levels of controllability. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys'18), pp. 13-21. 2018. [Best paper nominee]
  • Jin, Yucheng, Nava Tintarev, and Katrien Verbert. Effects of individual traits on diversity-aware music recommender user interfaces. In Proceedings of the 26th Conference on User Modeling, Adaptation, and Personalization (UMAP'18), pp. 291-299. 2018.
  • Millecamp, Martijn, Nyi Nyi Htun, Yucheng Jin, and Katrien Verbert. Controlling Spotify recommendations: effects of personal characteristics on music recommender user Interfaces. In Proceedings of the 26th Conference on user modeling, adaptation and personalization (UMAP'18), pp. 101-109. 2018.
  • Prehofer, Christian, Andreas Wagner, and Yucheng Jin. A model-based approach for multi-device user interactions. In Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MODELS'16), pp.13-23. 2016.
  • Jin, Yucheng, Karsten Seipp, Erik Duval, and Katrien Verbert. Go with the flow: effects of transparency and user control on targeted advertising using flow charts. In Proceedings of the international working conference on advanced visual interfaces (AVI'19), pp. 68-75. 2016.
  • Bongartz, Sara, Yucheng Jin, Fabio Paternò, Joerg Rett, Carmen Santoro, and Lucio Davide Spano. Adaptive user interfaces for smart environments with the support of model-based languages. In Proceedings of International Joint Conference on Ambient Intelligence (Aml'12), pp. 33-48. Springer, Berlin, Heidelberg, 2012.