Benedikt Loepp (former member)

Benedikt worked as a doctoral researcher and as a postdoc in the Interactive Systems group. His research focused at the intersection of human-computer interaction and machine learning, in particular, on recommender systems, including interactive approaches, user modeling, and preference elicitation methods. Moreover, he investigated novel methods to provide decision support in AI-based systems and in gaining a better understanding of their users.

Benedikt started a new position in September 2023 as an assistant professor (Juniorprofessor) at the University of Koblenz.

For more information, please visit Benedikt’s personal website.

Thesis

Contact

Mail
Web https://benedikt.loepp.eu

Selected publications

An integrated approach for transparent, multi-level decision support in interactive recommender systems

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

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

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

How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective

Loepp, B., & Ziegler, J. (2023). In J. Zhang & L. Chen (Eds.), Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 (pp. 1090–1095). Association for Computing Machinery, Inc.

Multi-list interfaces for recommender systems: Survey and future directions

Loepp, B. (2023). Frontiers in Big Data, 6.

User-centered recommender systems

Ziegler, J., & Loepp, B. (2023). In M. Augstein, E. Herder, & W. Wörndl (Eds.), Personalized Human-Computer Interaction (2nd ed., pp. 33–58). De Gruyter Oldenbourg.

How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective

Loepp, B., & Ziegler, J. (2023). RecSys ’23: Proceedings of the 17th ACM Conference on Recommender Systems.

Recommender Systems Alone Are Not Everything: Towards a Broader Perspective in the Evaluation of Recommender Systems

Loepp, B. (2022). PERSPECTIVES ’22: Proceedings of the 2nd Workshop on Perspectives on the Evaluation of Recommender Systems.

Towards Multi-Method Support for Product Search and Recommending

Kleemann, T., Loepp, B., & Ziegler, J. (2022). Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’22), 74–79.

Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components

Kleemann, T., Wagner, M., Loepp, B., & Ziegler, J. (2021). Mensch Und Computer 2021 – Tagungsband, 531–543.

On the Convergence of Intelligent Decision Aids

Loepp, B. (2021). Mensch Und Computer 2021 – Workshopband.

Interactive Methods for Model-based Collaborative Filtering Recommender Systems

Loepp, B. (2021). [PhD thesis, University of Duisburg-Essen].

Empfehlungssysteme

Ziegler, J., & Loepp, B. (2020). In T. Kollmann (Ed.), Handbuch Digitale Wirtschaft (pp. 717–741). Springer Gabler.

On the Use of Feature-based Collaborative Explanations: An Empirical Comparison of Explanation Styles

Naveed, S., Loepp, B., & Ziegler, J. (2020). ExUM ’20: Proceedings of the International Workshop on Transparent Personalization Methods Based on Heterogeneous Personal Data, 226–232.

Measuring the Impact of Recommender Systems – A Position Paper on Item Consumption in User Studies

Loepp, B., & Ziegler, J. (2019). Proceedings of the 1st Workshop on Impact of Recommender Systems (ImpactRS ’19).

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.

All 40 publication