Associated with the Linz Institute of Technology and the Institute of Computational Perception @ JKU Linz, Austria

Hybrid AI for Human-Centric Personalization (HyPer)

hyper Teaser

Abstract

Hybrid AI, which integrates symbolic and sub-symbolic methods, has emerged as a promising paradigm for advancing human-centric personalization. By combining machine learning with structured knowledge representations, hybrid AI enables interpretable and adaptive user models that account for human factors such as biases, mental models, and affective states. The HyPer workshop provides a venue to discuss how hybrid AI approaches, combining neural architectures, symbolic representations, and cognitive/behavioral frameworks, can bridge the gap between explainability, cognitive modeling, and automated adaptation to user preferences.


Citation

Elisabeth Lex, Kevin Innerebner, Marko Tkalcic, Dominik Kowald, Markus Schedl
Hybrid AI for Human-Centric Personalization (HyPer)
Proceedings of the 33nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP), doi:10.1145/3708319.3727563, 2025.

BibTeX

@inproceedings{ElisabethLex2025hyper,
    title = {Hybrid AI for Human-Centric Personalization (HyPer)},
    author = {Elisabeth Lex and Kevin Innerebner and Marko Tkalcic and Dominik Kowald and Schedl, Markus},
    booktitle = {Proceedings of the 33nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP)},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    doi = {10.1145/3708319.3727563},
    year = {2025}
}