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.
Due to the Internet's tremendous expansion in recent years, quite a few students have started studying on massive open online course platforms. The information explosion of online education platforms makes it challenging for users to choose their courses effectively. The course recommendation system has become the most effective way for online education platforms to solve the above problems. One of the classic algorithms used in recommendation algorithms is collaborative filtering, but it also has many limitations: 1) Collaborative filtering is affected by cold start and sparsity easily; 2) Collaborative filtering needs to be used for rating information on courses, but the real user data in the online education platform basically does have user rating information on courses nearly, so it is challenging to apply collaborative filtering in the field of course recommendation. So, we propose an algorithm and define a formula to calculate the user's degree of interest (rating) to the course based on the user's interaction information in this paper. It adopts the knowledge graph embedding representation learning method to embed the semantic information of the course into the low-dimensional semantic space, and then use the similarity calculation formula to get the acquaintance between users according to the embedded expression and perform collaborative filtering calculation. Our method is compared with user-based collaborative filtering and item-based collaborative filtering and the model is effective on real data, according to experimental results.
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