Recommender systems are able to estimate the interest for a user of a given resource from some information about similar users and resources properties. In our work, we focus on the recommendations of educational resources in the field of Technology Enhanced Learning (TEL) and more specifically the recommendations which are based on social information. Based on the results of research in recommender systems and TEL, we define an approach to recommend learning resources using social information present in social networks. We have developed a formal model for the calculation of similarity between users and the generation of three types of recommendation. We also developed a platform that implements our approach.