ACM UMAP is the premier international conference bringing together research in AI and HCI to support effective human-AI collaboration via interactive systems that can model, adapt and personalize to their users. The conference is sponsored by ACM SIGCHI and SIGWEB. User Modeling Inc., as the core Steering Committee, oversees the conference organization. UMAP operates under the ACM Conference Code of Conduct.

ACM UMAP 2025 will bring together researchers, practitioners, and policymakers from around the world to discuss the latest developments in human-centered approaches to design and use AI systems that adapt to human needs while ensuring user control, fairness, accountability, transparency, inclusivity and sustainability. The accepted papers will be published in the conference proceedings, accessible through the ACM Digital Library.

Topics of interest include but are not limited to

    Recommender systems
    Data mining techniques for user modeling, adaptation, and personalization
    Generative AI techniques for user modeling, adaptation, and personalization
    Large Language Models and Natural Language Processing methods for user modeling, adaptation and personalization
    Knowledge graphs, Linked data, and semantics for user modeling, adaptation, and personalization
    Intelligent user interfaces
    Intelligent and personalized e-learning applications and educational games
    Personalized behavior change and persuasive applications
    Modeling and adapting to human affective states
    Virtual assistants, conversational agents, personalization in augmented reality
    Group modeling and collaborative team formation
    Ethical issues of personalization and human-centered AI systems: Privacy, Fairness, Accountability, Transparency
    Creativity in User Modeling, Adaptation and Personalization
    Sustainability-aware methods and Sustainable Development Goals (SDGs) for user modeling, adaptation, and personalization
    Personalized approaches for preventing eco-chambers, user manipulation, and disinformation
    Research methods and reproducibility