TBD: To Be Determined
Objective
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 complex human factors such as biases, mental models, and affective states. The HyPer workshop focuses on how hybrid AI approaches—combining neural architectures, symbolic representations, and cognitive/behavioral frameworks—can foster more explainable and personalized user experiences. Specifically, we aim to explore innovative applications of hybrid AI in personalization, bridging the gap between explainability, cognitive modeling, and automated adaptation to user preferences. The HyPer workshop will provide a venue for researchers and practitioners to discuss the latest advancements, challenges, and future directions in this interdisciplinary field.
Important Dates
| Event | Date |
|---|---|
| Submission Site Open | March 6, 2025 |
| Paper Submissions | April 9, 2025 |
| Paper Notifications | April 28, 2025 |
| Camera-ready Submission | May 5, 2025 |
| Workshop date | June TBD, 2025 |
All deadlines are at 11:59 pm AoE (Anywhere on Earth).
Program
The workshop program will be announced later.
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 user modeling
- 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, e.g., by using symbolic approaches such as counterfactual fairness
- Behavioral data analysis and user studies of cognitively-inspired modeling approaches Domain-specific implementations of hybrid AI models (e.g., e-learning, healthcare, finance, music)
We welcome three types of submissions:
- Full research papers describing mature research results relevant to the workshop topics. Up to 8 pages (including references).
- Work-in-progress and Demo Papers describing ongoing research, preliminary research results, or demonstrations relevant to the workshop topics. Up to 6 pages (including references).
- Position and Perspective Papers, including position, discussion, reflection, and perspective papers on the workshop topics. Up to 4 pages (including references, if needed).
Please use the EasyChair Submission System to submit your contributions, selecting the “HyPer- Hybrid AI for Human-Centric Personalization” track. An international panel of experts will review all submissions.
Papers must be formatted according to the new workflow for ACM publications. All accepted papers will be published by ACM and will be available via the ACM Digital Library. At least one author of each accepted paper must register for the particular workshop and present the paper there.
The templates and instructions are available here. Use \documentclass[manuscript, review]{acmart} in the sample-sigconf-authordraft.tex file for double-column format.
If you work with Overleaf, you can directly start with the template from here. Use \documentclass[manuscript, review]{acmart} in the sample-sigconf-authordraft.tex file for double-column format.
