Network Course Recommendation System Based on Double-Layer Attention Mechanism

Abstract

In view of the lack of accurate recommendation and selection of courses on the network teaching platform in the new form of higher education, a network course recommendation system based on the double-layer attention mechanism is proposed. First of all, the collected data are preprocessed, while the data of students and course information are normalized and classified. Then, the dual attention mechanism is introduced into the parallel neural network recommendation model so as to improve the model’s ability to mine important features. TF-IDF (term frequency-inverse document frequency) based on the student score and course category is improved. The recommendation results are classified according to the weight of course categories, so as to construct different types of course groups and complete the recommendation. The experimental results show that the proposed algorithm can effectively improve the model recommendation accuracy compared with other algorithms.