Fused semantic information and hierarchical attention network for course recommendation
Tieyuan Liu, Rupeng Zhou, Chuangying Zhu, Liang Chang · 2023
With the development of smart education, personalized course recommendations are getting more and more attention. Recently, some in-depth learning-based solutions have been proposed. The main principle is to make sequential recommendations based on the learner's historical learning records, rarely use the semantic information covered by the course profile and user profile to complete the recommendation task. User preferences change over time. To solve these problems, a course recommendation model (FIHA) that integrates semantic information and hierarchical attention network is proposed. next class. Specifically, TextCNN is used to perform semantic processing on the course introduction to generate vectors, and then the learner's historical access course records are used to capture the learner's long-term and short-term preferences using hierarchical attention mechanism, and the learner's dynamic preference representation is obtained. The two are fused and recommendations are made based on their scores to courses. Experiments on real datasets show that this method is superior to other mainstream advanced methods.