Dr.-Ing. Tim Donkers, M. Sc.

After finishing his master’s program in 2017, Tim Donkers joined the group as a full member. His main research interests are recommender systems, deep learning and explainable AI.

Contact

Mail	tim [dot] donkers [at] uni-due [dot] de
Phone	+49 (203) 379-2716
Fax	+49 (203) 379-3557
Office	LE 410
LSF	view

Selected publications

From explanations to human-AI co-evolution: Charting trajectories towards future user-centric AI

Ziegler, J., & Donkers, T. (2024). i-Com: Journal of Interactive Media, 23(2), 263–272.

From explanations to human-AI co-evolution: Charting trajectories towards future user-centric AI

Ziegler, J., & Donkers, T. (2024). i-Com: Journal of Interactive Media, in press.

Harnessing Latent Space Semantics for Enhanced Interpretability of Recommender Systems in Item Retrieval and Online Communication Dynamics

Donkers, T. (2024). [PhD thesis].

De-sounding echo chambers: Simulation-based analysis of polarization dynamics in social networks

Donkers, T., & Ziegler, J. (2023). Online Social Networks and Media, 37-38.

An Instrument for measuring users’ meta-intents

Ma, Y., Donkers, T., Kleemann, T., & Ziegler, J. (2023). In J. Gwizdka & S. Y. Rieh (Eds.), CHIIR ’23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (pp. 290–302). ACM.

Meta-Intents in Conversational Recommender Systems

Yuan, M., Donkers, T., Kleemann, T., & Jürgen, Z. (2022). Proceedings of the 4th Edition of Knowledge-Aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2022.

Argumentative Explanations for Recommendations Based on Reviews

Hernandez Bocanegra, D. C. (2022). [PhD thesis, University of Duisburg-Essen].

The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending

Donkers, T., & Ziegler, J. (2021). Fifteenth ACM Conference on Recommender Systems, 12–22.

Leveraging Arguments in User Reviews for Generating and Explaining Recommendations

Donkers, T., & Ziegler, J. (2020). Datenbank-Spektrum, 20(2), 181–187.

Effects of Argumentative Explanation Types on the Perception of Review-Based Recommendations

Hernandez-Bocanegra, D. C., Donkers, T., & Ziegler, J. (2020). Adjunct Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20 Adjunct), 219–225.

Explaining Recommendations by Means of Aspect-Based Transparent Memories

Donkers, T., Kleemann, T., & Ziegler, J. (2020). In F. Paternò & N. Oliver (Eds.), Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 166–176). The Association for Computing Machinery.

Impact of Consuming Suggested Items on the Assessment of Recommendations in User Studies on Recommender Systems

Loepp, B., Donkers, T., Kleemann, T., & Ziegler, J. (2019). Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19), 6201–6205.

Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems

Kunkel, J., Donkers, T., Michael, L., Barbu, C.-M., & Ziegler, J. (2019). Proceedings of the 37th International Conference on Human Factors in Computing Systems (CHI ’19), 487:1–487:12.

Interactive Recommending with Tag-Enhanced Matrix Factorization (TagMF)

Loepp, B., Donkers, T., Kleemann, T., & Ziegler, J. (2019). International Journal of Human Computer Studies, 121, 21–41.

Impact of Item Consumption on Assessment of Recommendations in User Studies

Loepp, B., Donkers, T., Kleemann, T., & Ziegler, J. (2018). Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 49–53.

All 22 publication