An approach for learning resource recommendation using deep matrix factorization

Abstract

In traditional learning, learners and their lecturers, or tutors can meet face-to-face. In such lectures, the lecturers, or tutors can introduce printed book tutorials. However, in several circumstances, such as distance education, learners cannot interact with their teachers. Therefore, online learning resources would be helpful for learners to get knowledge. With a large and diverse number of learning resources, selecting appropriate learning resources to learn is very important. This study presents a deep matrix decomposition model extended from standard matrix decomposition to recommend learning resources based on learners' abilities and requirements. We test the proposed model on two groups of experimental data, including the data group of students' learning outcomes at a university for course recommendation and another group of 5 datasets of user learning resources to provide valuable recommendations for supporting learners. The experiments have revealed promising results compared to some baselines. The work is expected to be a good choice for large-scale datasets.