A hybrid approach for course recommendation: leveraging collaborative filtering and knowledge graphs

Abstract

In education systems, learners face the challenge of selecting appropriate courses that align with their academic goals or training needs. This paper introduces a hybrid course recommendation system that combines collaborative filtering and knowledge graphs to provide personalised and explainable course suggestions that guide students through their enrollment process. Unlike traditional recommendation methods, which often lack transparency and may offer unsuitable courses for students with different enrollment needs, our system helps prevent redundant suggestions and ensures that students receive appropriate course options based on their preferences and interactions. The system uses a Bernoulli matrix factorisation model to predict the course status, and a filtering method through information extracted from a knowledge graph to provide students with an effective and explainable recommendation. Empirical evaluation of a real-world dataset of 36.6K student records indicates that the proposed system achieves slightly higher levels of precision, recall and F1-score compared to Dirichlet matrix factorisation, naive Bayes collaborative filtering and neural collaborative filtering models. Furthermore, the Bernoulli matrix factorisation model also achieves good performance using the normalised discounted cumulative gain quality measure. The integration of the Bernoulli matrix factorisation model with knowledge graphs offers course recommendations that are accurate, diverse and transparent, improving the learning experience.