Effects of Quantitative Explanations on Fairness Perception in Group Recommender Systems

Group recommender systems (GRS) aim to deliver recommendations to groups of individuals, assisting them in planning activities such as going to the cinema with friends, organizing a family vacation, or dining out with colleagues.Unlike traditional recommender systems (RS), GRS must account for the preferences of multiple individuals, often balancing potentially conflicting goals.In this context, it is crucial to provide recommendations that are perceived as fair by all group members.While numerous aggregation strategies have been proposed, understanding users' perspectives on fairness remains an open challenge.In this paper, we present the results of a user study in which real participants acted as external judges, evaluating the fairness of group recommendations.The study investigates the impact of quantitative explanations, conditioned by specific GRS and group types, on fairness perception.Our findings suggest that, without additional information, the task may be too difficult for users, and their ability to distinguish between different group types is limited, further underscoring the importance of explanations.Study data are available from https://osf.io/9fpyr/.