Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.

Group recommender systems (GRSs) are tools that support a group to find items that the whole group would enjoy experiencing jointly. There are two main lines of research in this field. The first line of research focuses on methods that combine the preferences of individual group members to obtain a group preference model and generate appropriate recommendations. The second line of research is more holistic and aims to support groups in all the phases of their decision-making process. The majority of the approaches of the second type use a simple conversational approach, which is critiquing. However, nowadays people heavily rely on social and chat platforms to make group decisions, and we believe that these platforms could be a valuable mean for building more effective GRSs. To this end, we have designed a framework tool that extends standard chat platforms by augmenting it with a chat-bot. The chat-bot enables the communication between the users on one side and the group recommender agent on the other. Our goal is a new holistic approach to group recommendations that would be the more beneficial than previous proposed conversational approaches. We aim to provide the proposed framework as an open environment for researchers to prototype their own GRSs.

Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems’ modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work.

In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.