A Recommendation Approach in Social Learning Based on K-Means Clustering

Social networks are a powerful and efficient tool for e-learning promoting collaboration between learners. Thus, to better manage the learning process within these environments, it is imperative to use recommendation systems which take a very significant role in suggesting interesting material adapted to the different needs of learners. To model the recommendation systems, the researchers relied on numerous tools such as the exploitation of Machine Learning algorithms or social interactions between learners. Yet, behaviour within a social network can actually differ from one learner to another, so we will be dealing with several categories of learners with distinct attitudes. Based on this, we raise a rather important issue which is to classify the learners according to well-defined criteria and attitudes before calculating the recommendations. In the recommendation system we advocate, we therefore use the k-means algorithm to classify learners, then we calculate the recommendations for each cluster by referring to our old recommendation system proposed in one of our previous works. The global system is thus based on three essential points: k-means, correlation and co-occurrence. We then evaluate the performance of our proposed system in order to show its performance compared to the system that does not consider the k-means algorithm.