Objective

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.

The workshop proposal can be found here.

Important Dates

Event	Date
Paper Submissions	August 24, 2026
Paper Notifications	September 23, 2026
Workshop date	November 8, 2026

All deadlines are at 11:59 pm AoE (Anywhere on Earth).

Program

tbd

Call for Contributions

The Hyper workshop aims to bridge the gap between sub-symbolic learning (e.g., neural networks) and symbolic knowledge representations (e.g., knowledge graphs, ontologies, logic-based models) to develop hybrid user models that better reflect human cognitive processes, social behaviors, and decision-making patterns.

We invite research papers (short and long), extended abstracts, and position papers relevant to the workshop topics, which include, but are not limited to:

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

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).