Enhancing Collaborative Filtering with Game Theory for Educational Recommendations: The Edu-CF-GT Approach

Abstract

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.