ExUM 2026: 8th Workshop on Explainable User Modeling and Personalised Systems
Musto, C., Delić, A., Polignano, M., Rapp, A., Semeraro, G., & Ziegler, J. (2026). In Association for Computing Machinery (ACM) (Ed.), Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’26) (pp. 674–676). ACM.
Abstract
Adaptive and personalized systems increasingly mediate everyday digital experiences, and recent advances in LLMs, NLP, and Generative AI have amplified their reach, from intelligent user interfaces and conversational agents to AR/immersive interactions and autonomous assistants. These methods now support applications in health and well-being, behavior change and persuasion, e-learning and educational games, and group modeling for collaboration and team formation, all of which require increasingly rich and dynamic user models. At the same time, modern pipelines based on data mining, knowledge graphs/linked data, semantic representations, and affective computing raise urgent questions about transparency, privacy, fairness, accountability, and user understanding, reinforced by regulatory expectations such as the GDPR right to explanation. Yet research often optimizes personalization performance without comparable attention to interpretability and human comprehension. This workshop provides a forum for theoretical, methodological, and empirical work that bridges effectiveness and explainability, with emphasis on robust human-centered evaluation and reproducible practices, including benchmarks, datasets, and shared challenges that advance trustworthy personalization in an era of increasingly autonomous AI.