A novel recommender system based on two-level friendship ties within social learning

Abstract

Nowadays, social networks are starting to emerge as a huge part of e-learning. Indeed, learners are more attracted to social learning environments that foster collaboration and interaction among learners. To enable learners to handle their time and energy more effectively, recommendation systems tend to address these issues and provide learners with a set of recommendations appropriate to their needs and requirements. To this end, we propose a recommendation system based on the correlation and co-occurrence between the activities performed by the learners on one hand, and on the other hand, based on the community detection based on two-level friendship ties. The idea is to detect communities based on friends and friends of friends, and then generate recommendations for each community detected. We test our approach on a database of 3000 interactions and it turns out that the two-level recommendation system based on friendships reaches a high accuracy and performs better results than the recommendation system based one level friendship ties in terms of precision as well as accuracy. It turns out that expanding the detected communities to generate new communities leads to more relevant and reliable results.