Clustering collaborative filtering approach for Diftari E-Learning platform' recommendation system

Recommendation systems are among the most interesting systems which simplify web or search experiences for users by selecting specific items related to their interests from a wide range of choices for them. The use of such systems in e-learning has helped learners filter choices and select content through the utilization of different methods such as the content-based approach and the collaborative approach; however, these methods have not reached an optimal satisfaction among learners about the offer provided. Hence, in this study, we try to propose a different approach based on clustering to make up for the shortcomings of the previously mentioned methods. The main objective of this proposed approach is to get homogeneous groups of learners, and eventually assure that the items recommended are all covered and assimilated by learners.