Deep Matrix Factorization for Learning Resources Recommendation

Learning should last all through people’s lives. With traditional learning, learners can meet face-to-face with their teachers or tutors. However, in some circumstances, learners cannot interact with their teachers. Learning resources (e.g., books, journals, slides, etc.) would be helpful for learners to get knowledge. With a large number of learning resources, how to select appropriate learning resources to learn is very important. In this work, a deep matrix factorization model extended from the standard matrix factorization is proposed for learning resources recommendation. We validate the proposed model on five published learning resources datasets and compare it with other well-known methods in recommender systems. The experimental results show that the proposed deep matrix factorization model works well, especially it can be a good choice for large-scale datasets.