A Computational Model for Promoting Targeted Communication and Supplying Social Explainable Recommendations

An emerging paradigm of Explainable Recommender Systems (ERS) leverages social friend information to supply users with his/her friends' public interests as explained recommendation. In this work, we consider this issue from a theoretical pont of view. We define a computational problem aimed to support the formation of communities of patients (users) and health services (items), thus stimulating targeted communication in social spaces. Using this formulation, dedicated ERS can benefit from social information to supply optimized recommended clarification. In particular, we report the conceptual framework with numerical results applying random graph models.