The increasing adoption of personalization and recommender systems in high-impact domains raises fundamental questions about how user models represent, reason about, and adapt to human behavior. While recent advances in machine learning have improved predictive accuracy, they offer limited support for explicit reasoning, interpretability, and the incorporation of cognitive and normative constraints. The Second International Workshop on Hybrid AI for Human-Centric Personalization and Recommendation (HyPeR 2026) focuses on hybrid AI as a methodological framework for user modeling, recommendations, and personalization, emphasizing the integration of learning-based approaches with symbolic knowledge, reasoning mechanisms, and cognition-informed representations. HyPeR brings together researchers and practitioners to examine architectures, methods, and evaluation strategies for hybrid user models. The workshop aims to stimulate exchange and to shape future research directions at the intersection of user modeling, hybrid AI, and human-centered recommender systems.

Submission Guidelines

We welcome three types of submissions (in single-column CEUR-WS workshop template):

• Full research papers describing mature research results relevant to the workshop topics. Up to 12 pages (excluding references).
• Short (Work-in-progress and Demo) Papers describing ongoing research, preliminary research results, or demonstrations relevant to the workshop topics. Up to 6 pages (excluding references).
• Position Papers of novel ideas, including position, discussion, reflection, and perspective papers on the workshop topics. Up to 6 pages (excluding references, if needed).

List of Topics

• Methods for integrating symbolic knowledge and sub-symbolic learning in recommendation systems
• Applications of cognitive theories and behavioral insights in hybrid AI models for personalization
• Techniques for interpretability, explainability, and trust in hybrid AI systems
• Methods for detecting and mitigating biases and unfairness in hybrid AI using symbolic approaches (e.g., counterfactual fairness)
• Mechanisms for dynamic adaptation and symbolic reasoning to handle evolving user preferences and context within hybrid architectures
• Evaluation procedures and standardized benchmarks for assessing the performance and robustness of hybrid AI in recommender systems
• Behavioral data analysis and user studies of cognition-informed and hybrid modeling approaches
• Domain-specific implementations of hybrid AI models in areas such as e-learning, healthcare, finance, and music
• Real-world systems and case studies demonstrating hybrid AI architectures for personalized recommendations

Organizers

• Elisabeth Lex (Graz University of Technology)
• Markus Reiter-Haas (Graz University of Technology)
• Marko Tkalčič (University of Primorska)
• Dietmar Jannach (University of Klagenfurt)
• Markus Schedl (Johannes Kepler University Linz, LIT AI Lab)

Publication

HyPeR 2026 proceedings will be published in CEUR Workshop Proceedings

Venue

The workshop will be held on November 8 @ CIKM 2026 in Rome, ITALY

Contact

All questions about submissions should be emailed to Markus Reiter-Haas (reiter-haas [at] tugraz . at).