Peter Brusilovsky (School of Information Sciences, University of Pittsburgh), Marco de Gemmis (University of Bari “Aldo Moro”), Alexander Felfernig (Institute for Software Technology, Graz University of Technology), Pasquale Lops (University of Bari “Aldo Moro”), Marco Polignano (University of Bari “Aldo Moro”), Giovanni Semeraro (University of Bari “Aldo Moro”), Martijn C. Willemsen (Eindhoven University of Technology)

The IntRS workshop series focuses on user-centric perspective on recommender systems research. The IntRS workshop brings together an interdisciplinary community of researchers and practitioners who share research on new recommender systems (informed by psychology), including new design technologies and evaluation methodologies, and aim to identify critical challenges and emerging topics in the field. Indeed, the workshop focuses particularly on the impact of interfaces on decision support and overall satisfaction, and it is also connected to the topics of Human-Centered AI, Explainability of decision-making models, User-adaptive XAI systems, which are becoming more and more popular in the last years especially in domains where recommended options might have ethical and legal impacts on users. The integration of XAI with recommender systems is crucial for enhancing their transparency, interpretability, and accountability. XAI can help users understand why a particular recommendation is made, what data and algorithms are used, and what factors influence the outcome. This can increase the user’s trust and confidence in the system, and improve their satisfaction and engagement with the recommendations. The explanations should be presented in a way that is understandable, concise, and relevant to the user’s context and goals. This requires collaboration between XAI researchers, designers, and end-users to ensure that the explanations meet the user’s expectations and needs. An interesting research direction that has recently received renewed interest is to investigate how users interact with recommenders based upon their cognitive model of the system. Previous work, investigated the impact of users’ mental models of recommender systems on their interactions with them and drew a theory to understand the key determinants motivating users to such user behavior. We believe that the paradigm that describes the relationship between humans and recommender systems is changing and evolving from a “human-centered” design approach toward a symbiotic vision. From this point of view, the mutual exchange of knowledge between human and system will lead us towards “symbiotic recommender systems”, in which both parties learn by observing each other. We hope IntRS will be the forum where fresh ideas on this topic will be discussed.