Abstract
The present work fits into the context of recommender systems for educational resources, especially systems that use social information. Based on the research results in the field of recommender systems, social networks and technology-enhanced learning, we defined an educational resource recommendation approach. We rely on social relations between learners to improve recommendation accuracy. Our proposal is based on formal models that generate three types of recommendation, namely recommendation of popular resources, useful resources and recently viewed resources. We developed a learning platform which integrates our recommendation models. In this paper, we present the results of an experiment conducted during six months in a real educational context. The goal of this experiment is to measure the relevance, quality and utility of the recommended resources. We also conduct an offline analysis by using a dataset in order to compare our approach with four baseline algorithms.