Workshops
6th Workshop on Adapted intEraction with SociAl Robots (cAESAR)
Organizers: Francesca Cocchella, Alberto Lillo, Giuseppe Palestra, Luca Raggioli, Giulia Scorza Azzarà and Cristina Gena
Description: Human-Robot Interaction (HRI) is a field dedicated to understanding, designing, and evaluating robotic systems intended for use by or with humans. An important issue for a successful introduction of robots in everyday life use concerns with their capability to adapt to their human counterparts, understanding their needs, fostering seamless collaboration, and being perceived as considerate and reliable partners in shared environments. This is of particular importance in the field of social robotics and social HRI, where the robot should adapt to emotions, personalities, and it should also have a memory of past interactions with the user to become believable. The aim of this Workshop is to bring together researchers and practitioners who are working on various aspects of social robotics and adaptive interaction. The expected result of the workshop is a multidisciplinary research agenda that will inform future research directions and hopefully, forge some research collaborations.
7th Workshop on Explainable User Models and Personalised Systems (ExUM 2025)
Organizers: Cataldo Musto, Marco Polignano, Amon Rapp, Giovanni Semeraro and Jürgen Ziegler
Description: ExUM 2025 aims to create a forum for discussing the pressing challenges, innovative methodologies, and future directions in exploring how transparency, explainability, and user-centric design can be incorporated into novel technologies, including LLMs, to make them not only effective but also trustworthy, ethical, and aligned with the diverse needs and expectations of their users.
7th UMAP Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2025)
Organizers: Bamshad Mobasher, Styliani Kleanthous, Robin Burke, Avital Shulner-Tal and Tsvi Kuflik
Description: Machine learning, recommender systems, and user modeling are essential for personalized intelligent systems, but they also raise ethical, policy, and legal challenges. Metrics used to optimize data-driven models often fail to capture fairness, transparency, diversity, and other social welfare considerations. While recent research has focused on bias, fairness, and transparency in machine learning, more work is needed to integrate these concerns into user modeling and personalization. The 7th edition of this workshop will bring together experts from academia and industry to explore mechanisms for mitigating bias and ensuring fairness in personalized systems, aligning with UMAP 2025’s themes for a deeper, interactive discussion.
GMAP 2025: 4th Workshop on Group Modeling, Adaptation and Personalization
Organizers: Francesco Barile, Amra Delić, Ladislav Peska, Isabella Saccardi and Cedric Waterschoot
Description: While most existing HCI and decision-support systems are designed to support single users, there are scenarios where these systems should consider the needs of groups. In these cases, specific challenges have to be addressed. Collective factors – such as interpersonal relationships, group mood, and emotional contagion – play a crucial role in group dynamics. Still, they are often ill-defined and absent from systems’ modeling. Furthermore, producing fair, privacy-protecting, and explainable recommendations is a notorious challenge of group recommender systems. The potential of large language models to enhance explainability and tackle these challenges is still under-explored. Lastly, the problem of defining a comprehensive evaluation methodology that covers the particularities of group recommender systems is a long-standing issue in the field. The 4th Workshop on Group Modeling, Adaptation and Personalization (GMAP) aims to bring together a community of researchers from multiple disciplines, including Psychology, Computer Science, and Organizational Behavior. In this workshop, researchers have the opportunity to share research and ideas, fostering a vibrant and inclusive community and creating opportunities for networking and collaboration. Such communities will contribute to advancing our understanding of group modeling, adaptation and personalization, identifying key challenges and opportunities, and developing a shared research agenda to guide future works in the field.
Hybrid AI for Human-Centric Personalization (HyPer)
Organizers: Elisabeth Lex, Kevin Innerebner, Marko Tkalcic, Dominik Kowald and Markus Schedl
Description: 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.
LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization
Organizers: Ashmi Banerjee, Yashar Deldjoo, Thomas Kolb, Julia Neidhardt and Ahmadou Wagne
Description: Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. LLM4Good is a half-day workshop dedicated to addressing these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. The workshop brings together researchers and practitioners to discuss energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. It also provides a forum for examining broader meta topics, including sustainable LLM design, trustworthy personalization approaches, innovative generative and conversational applications, novel evaluation methodologies, and the societal impact of these technologies. In alignment with the Sustainable Development Goals and Digital Humanism principles, LLM4Good aims to advance the development of trustworthy, human-centric LLM systems that can positively influence education, healthcare, and other key domains.
The 16th International Workshop on Personalized Access to Cultural Heritage (PATCH 2025)
Organizers: Tsvi Kuflik, Alan Wecker, Noemi Mauro and Liliana Ardissono
Description: Following the successful series of PATCH workshops, PATCH 2025, the new link in the long chain of PATCH workshops series, will be again the meeting point between state-of-the-art cultural heritage research and personalization – using any kind of technology, while focusing on ubiquitous and adaptive scenarios, to enhance the personal experience in cultural heritage sites. The workshop aims to bring together researchers and practitioners working on various aspects of cultural and natural heritage (CH / NH) who are interested in exploring the potential of state-of-the-art ICT technology (onsite and online) to enhance the experience of the visit. The expected result of the workshop is a multidisciplinary research agenda that will inform future research directions and hopefully, forge some research collaborations.
WeBIUM 2025: 2nd Workshop on Wearable Devices and Brain-Computer Interfaces for User Modelling
Organizers: Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Angela Lombardi, Tommaso Di Noia and Fedelucio Narducci
Description: Wearable Devices (WDs), such as smartwatches and fitness trackers, generate continuous data streams that offer valuable insights into physiological states, activity patterns, and user interactions. Meanwhile, Brain-Computer Interfaces (BCIs) provide access to cognitive and emotional states, with recent advancements enabling their integration into wearable formats. Despite their potential, fully leveraging these data sources for user modelling and personalization remains underexplored. This workshop explores the convergence of WDs, BCIs, and Large Language Models (LLMs) to enhance user modelling by synthesizing multimodal data. Discussions will address opportunities, challenges, and ethical considerations in utilizing sensitive physiological and neural data. Through interdisciplinary collaboration, the initiative aims to advance adaptive systems that dynamically respond to individual needs, fostering innovation in personalized experiences.