FARGO: A Fair, Context-AwaRe, Group RecOmmender System.

Lots of activities, like watching a movie or going to the restaurant, are intrinsically group-based. To recommend such activities to groups, traditional single-user recommendation techniques cannot be adopted, as a consequence, over the years, a number of group recommender systems have been developed. Recommending to groups items to be enjoyed together poses many ethical challenges, in fact, a system whose unique objective is to achieve the best recommendation accuracy possible, might learn to disadvantage submissive users in favor of more aggressive ones. In this work we investigate the ethical challenges of context-aware group recommendations, in the more general case of ephemeral groups (i.e., groups where the members might be together for the first time), using a method that can recommend also items that are new in the system. We show the goodness of our method on two real-world datasets. The first one is a very large dataset containing the personal and group choices regarding TV programs of 7,921 users w.r.t. sixteen contexts of viewing. The second one, which has been collected specifically for this work and that is made publicly available as one of the contributions of this article, gathers the musical preferences (both individual and in groups) of 280 real users w.r.t. two contexts of listening. We compare the results of our approach with seven other group recommender systems specifically developed to be fair. We evaluate the goodness of our recommendations using recall, while their fairness is assessed using two measures found in the literature, namely, score disparity and recommendation disparity. Our extensive experiments show that our method always manages to obtain the highest recall while delivering ethical guarantees in line with the other fair group recommender systems tested.