Submission Guidelines

This workshop aims to explore the integration of large language models (LLMs) with multi-modal data sources, such as text, images, audio, and behavioral data, to advance personalized and adaptive user modeling. The rapid evolution of LLMs has opened new avenues for leveraging diverse data modalities to create more accurate, context-aware user models across applications like healthcare, personalized recommendation systems, intelligent tutoring, and human-computer interaction. This workshop provides a platform for researchers, practitioners, and industry professionals to present innovative ideas, share practical implementations, and discuss challenges and future directions in this emerging field. The following paper categories are welcome:

• Multi-modal data integration for user modeling
• Applications of LLMs in personalized and adaptive systems
• Novel architectures combining LLMs with multi-modal data
• Ethical and privacy considerations in multi-modal user modeling
• Evaluation techniques for multi-modal user models
• Case studies in healthcare, education, or recommendation systems
• Benchmark datasets and tools for multi-modal modeling

Submissions must be in English, in double-column format, and must adhere to the ACM template and format (also available in Overleaf). Word users may use the Word Interim Template and the recommended setting for LaTeX is:

\documentclass[sigconf, anonymous, review]{acmart}.

Submissions must be a single PDF file: 4 (four) to 8 (eight) pages, with up to 2 additional pages for references and an optional Appendix (that can contain details on reproducibility, proofs, pseudo-code, etc).

Papers accepted by a workshop can be included in the Companion Proceedings of the Web Conference 2025, subject to meeting the camera-ready timeline. Workshop papers that have been previously published or are under review for another journal, conference or workshop should not be considered for publication.

Committees

Organizing committee

• Mohammad Ali Moni, The University of Queensland, Australia
• Zhicheng Lu, Charles Sturt University, Australia
• Vera Yuk Ying Chung, The University of Sydney, Australia
• Weidong Cai, The University of Sydney, Australia
• Xiaoming Chen, Beijing Technology and Business University, China

Contact

All questions about submissions should be emailed to mmoni@csu.edu.au