* We apologize if you receive multiple copies of this email * * Online version (registration): https://www.um.org/umap2024/registration/ * Online version (tutorials): https://www.um.org/umap2024/tutorials/ ACM UMAP 2024: The 32nd ACM Conference on User Modeling, Adaptation and Personalization Cagliari, Sardinia, Italy July 1-4, 2024 There’s still time to register for UMAP 2024 Conference! - Registration information: https://www.um.org/umap2024/registration/ - Standard Registration until June 21, 2024 - Onsite Registration By July 4, 2024 * Registration Link: https://cvent.me/ekykDz *This year, UMAP 2024 will include 5 tutorials:* *Trustworthy User Modeling and Recommendation From Technical and Regulatory Perspectives* Several efforts in Europe (e.g., the AI Act) and beyond have highlighted the significance of regulating AI technology, which has substantial implications for research and development in the areas of user modelling and recommender systems. It particularly requires actions to enhance the trust of various stakeholders in the processes and outcomes of corresponding R&D activities. This 90-minute tutorial will address these implications from both technical and regulatory perspectives. It will provide an interdisciplinary overview of recent regulations, with a specific focus on the core areas of (1) fairness and non-discrimination, (2) privacy and security, and (3) transparency and explainability. The tutorial will empower its audience with a deep understanding of the social and ethical consequences of their work, and of recent ethical guidelines and regulatory frameworks addressing the aforementioned dimensions. It will discuss relevant research and offer practical examples that address the aforementioned trustworthiness aspects. Furthermore, it will demonstrate how new regulations impact the daily work of the audience. * Website: https://socialcomplab.github.io/Trustworthy-UMAP-Tutorial-24/ *Collaborative Team Recommendation for Skilled Users: Objectives, Techniques, and New Perspectives* Collaborative team recommendation involves selecting users with certain skills to form a team who will, more likely than not, accomplish a complex task successfully. To automate the traditionally tedious and error-prone manual process of team formation, researchers from several scientific spheres have proposed methods to tackle the problem. In this tutorial, while providing a taxonomy of team recommendation works based on their algorithmic approaches to model skilled users in collaborative teams, we perform a comprehensive and hands-on study of the graph-based approaches that comprise the mainstream in this field, then cover the neural team recommenders as the cutting-edge class of approaches. Further, we provide unifying definitions, formulations, and evaluation schema. Last, we introduce details of training strategies, benchmarking datasets, and open-source tools, along with directions for future works. * Website: https://fani-lab.github.io/OpeNTF/tutorial/umap24/ *DECI: Designing Effective Conversational Interfaces* The rise in popularity of conversational agents has enabled humans to interact with machines more naturally. There is a growing familiarity among people with conversational interactions mediated by technology due to the widespread use of mobile devices and messaging services. Over half the population on our planet has access to the Internet with ever-lowering barriers to accessibility. Though text modality is a dominant way to implement CUIs today, foundational AI models enable the implementation of multimodal CUIs using voice and visual modality. Adopting visual and auditory cues in addition to text-based responses provides an engaging user experience, specifically in complex scenarios like health guidance, and job interviewing, among others. This tutorial will present a review of state-of-the-art research and best practices on building and deploying multimodal CUIs and synthesize the open research challenges in supporting such CUIs. The tutorial will also showcase the benefits of employing novel conversational interfaces in the domains of human-AI decision-making, health and well-being, information retrieval, and crowd computing. We will discuss the potential of conversational interfaces in facilitating and mediating the interactions of people with AI systems. * Website: https://sites.google.com/view/decitutorial *Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends* The tutorial is designed to act as a detailed guide through the evolving field of user modeling research, highlighting the significant changes that have recently reshaped this area of study. We provide an overview of the vast and continuously expanding areas of user modeling and profiling, covering both their historical development and technical aspects. Our goal is to clarify the meanings of each crucial term in this field, thereby reducing misunderstandings and misinterpretations. At the heart of our tutorial, we will delve into the significant paradigm shifts witnessed in the last few years, primarily driven by advances in technology, along with the latest research trends and innovative directions in the domain. We explore and elaborate on progress in areas such as user behavior modeling, user representation, and beyond-accuracy perspectives. Throughout the presentation, we intend to actively involve the audience in discussions to promote an interactive and engaging learning experience. * Website: https://link.erasmopurif.com/tutorial-umap24 *Mastering Mind and Movement. ACM UMAP 2024 Tutorial on Modeling Intelligent Psychomotor Systems (M3@ACM UMAP 2024)* Research in the psychomotor field to provide personalization support to users represents several research challenges. The objective of the M3@ACM UMAP 2024 tutorial is to provide all level researchers of the UMAP community with methodologies, tools and techniques to model complex psychomotor behaviours that can later personalize learning support in realms like sports, physical education or for rehabilitation purposes, providing insights into data gathering from activities that involve human movements. In the M3 tutorial we will focus on how to take advantage of a learning analytics platform to support data engineering processes applied to the psychomotor domain, thus allowing for a practical and guided experience to the tutorial participants. Participants will engage in hands-on activities, recording specific movements and learning how to capture human body keypoints and model psychomotor learning. * Website: https://phyum.uned.es/m3umap24/