Personalized Course Recommendation Based on Eye-Tracking Technology and Deep Learning

With the rapid development of online courses, the requirements of personalized course recommendation have been increasing. The traditional collaborative filtering algorithm confronts with the challenge of cold start, which is difficult to settle on online course recommendation effectively. In this paper, we propose a novel click through rate (CTR) model for personalized online course recommendation, with discriminative user features, item features and cross features. The feature representation ability of the CTR model is improved and the serious challenge of cold start is alleviated. Furthermore, transfer learning is introduced to deal with the problem of insufficient data in models training. More specially, eye tracking technology is applied to capture the users' cognitive styles, which are visualized with the heat map and fixation point trajectory. Finally, the recommendation interface sent to the learners, according to the user's cognitive style. The experiments show that the novel CTR model improves the performance of the personalized online course recommendation.