Prof. Dr.-Ing. Jürgen Ziegler

Jürgen Ziegler is a full professor in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen where he directs the Interactive Systems Research Group. His main research interests lie in the areas of human-computer interaction, human-AI cooperation, recommender systems, information visualization, and health applications.

Jürgen Ziegler holds a diploma degree in electrical engineering and biocybernetics from the University of Karlsruhe and a doctoral degree from the University of Stuttgart. Prior to joining the University, he was head of the Competence Center for Software Technology and Interactive Systems at the Fraunhofer Institute for Industrial Engineering in Stuttgart. Among various other scientific functions he is currently editor-in-chief of i-com - Journal of Interactive Media (De Gruyter) and chair of the German Special Interest Group on User-Centred Artificial Intelligence.

Journal of Interactive Media

For a description of the scope of the journal and submission see i-com.

Contact

Mail
Phone +49 (203) 379-2270
Fax +49 (203) 379-3557
Office LE 410
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Selected publications

Meta-Intents in Conversational Recommender Systems

Psychological User Characteristics and Meta-Intents in a Conversational Product Advisor

Towards Multi-Method Support for Product Search and Recommending

Health Recommender Systems for Mental Health Promotion

Development of an Instrument for Measuring Users’ Perception of Transparency in Recommender Systems

Editorial

An Interactive Hybrid Approach to Generate Explainable and Controllable Recommendations

Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System

ConvEx-DS: A Dataset for Conversational Explanations in Recommender Systems

Identifying Group-Specific Mental Models of Recommender Systems: A Novel Quantitative Approach

Effects of Interactivity and Presentation on Review-Based Explanations for Recommendations

Mixed-Modality Interaction in Conversational Recommender Systems

All 264 publication