Attention-Based CNN for Personalized Course Recommendations for MOOC Learners

Massive Open Online Courses (MOOCs), which are open for anyone without limitations on time or location, have attracted millions of registered online students. The large number of online courses available raises the question of how appropriate courses can be effectively recommended to interested learners. The recommendation system, widely used in various online applications, is a good solution for reducing decision complexity. In this paper, we propose the method of using attention-based convolutional neural networks (CNN) to obtain a user's profile, predict the user ratings, and recommend the top-n courses. First, we represent the learner behaviors and learning histories into feature vectors. The attention mechanism is then used to improve relevance estimation according to the differences between the estimation scores and the actual scores given by users to train the neural network. Finally, the trained model will recommend courses to learners. At the end of the paper, we introduce the framework of our system.