An Argumentative Framework for Generating Explainable Group Recommendations

In the context of group recommender systems, explanations strategies have been proposed to improve recommendations perceived fairness, consensus, satisfaction, and to help the group members in the decision-making process. In general, such explanations try to clarify the underlying social chioce-based aggregation strategies used to generate the recommendations. However, results in the literature are conflicting, and the real benefit of such explanations seem to be limited. In this work, we propose a novel approach, which makes use of an argumentative framework built using information about the aspects that are connected to the recommended items. Such framework is used to generate recommendations, and related explanations. We provide a proof of concept on how to generate explanations for the group, as well as specific explanations for the group members, which use the information in the argumentative frameworks to enrich the explanations. Furthermore, we propose privacy-preserving versions for the explanations, as well as a graphical approach based on tag clouds. In future works, we plan to evaluate the quality of the provided recommendations in offline settings, as well as the impact of the proposed explanations in a series of user studies.