Attentional neural mechanisms for social recommendations in educational platforms

Recent studies in the context of machine learning have shown the effectiveness of deep attentional mechanisms for identifying important communities and relationships within a given input network. These studies can be effectively applied in those contexts where capturing specific dependencies, while downloading useless content, is essential to take decisions and provide accurate inference. This is the case, for example, of current recommender systems that exploit social information as a clever source of recommendations and / or explanations. In this paper we extend the social engine of our educational platform “WhoTeach” to leverage social information for educational services. In particular, we report our work in progress for providing “WhoTeach” with an attentional-based recommander system oriented to the design of programmes and courses for new teachers.