Hybrid Recommender System for Personalized Pedagogical Resource Recommendations in E-Learning Platforms
Yassamina Mediani, Mohamed Gharzouli · Ingénierie des systèmes d information · 2024
Recommender systems are generally used in several domains, like e-commerce sites and social networks.E-learning systems use recommendation techniques to facilitate and improve online learning.Educational platforms offer users the necessary pedagogical tools to create an enriched learning environment, fostering collaboration and resource sharing.Recommender System faces many challenges.Among issues: (1) cold-start in which new users and/or items having not prior information available in the system; (2) data sparsity where rated items number is very small contrary to unrated items; and (3) scalability where more training data is required.This study presents a recommender system that uses learner criteria, such as learner's past behavior, demographics information, performance data, collaborative filtering, and ratings to suggest pedagogical resources.The proposed system adopts a hybrid approach, combining two primary methods: popularity-based and collaborative filtering-based.This hybrid approach enhances a collaborative filtering approach with popularity to provide a starting point for new users.The popularity-based is specifically used to address the issue of cold-start for new users by providing primary recommendations.Additionally, we have used two collaborative filtering approaches.The SVD-based enhances the recommendation list for the new user and tackles the sparsity problem.Simultaneously, enhanced matrix factorization with deep neural network (DNN) outperforms traditional matrix factorization in terms of recommendation diversity and accuracy.Our system improves the accuracy and effectively responds to user needs.Our approach early findings show promising results.It scores for top-10 items a total recall of (0.47), a global precision of (0.20), and an accuracy of (0.87).