In the field of education, the proliferation of e-learning platforms has considerably increased access to teaching material. However, this abundance of resources poses a serious challenge to learners in the form of information overload that hinders the learning process. To meet this challenge, effective mechanisms need to be put in place to guide learners towards resources that are tailored to their individual needs and preferences. Recommendation systems appear to be essential tools in this context, aiming to personalise the learning experience by offering targeted suggestions based on the user’s preferences. This article presents EDU–CF–GT, a new educational recommendation model, as a solution to this challenge. Based on our generic CF–GT model, EDU–CF–GT is adapted to the complexities of the educational domain, improving learning efficiency by simplifying access to resources. Through evaluation on an educational dataset, EDU–CF–GT demonstrates significant improvements in recommendation relevance and learner satisfaction compared to existing models.

Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.