Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization

Course recommendation systems are applied to help students with different needs select courses in a large range of course resources. However, a student's needs are not always determined by their personal interests, they are also influenced by teachers, peers etc. Unlike online courses, user behavior and user satisfaction of offline courses often have serious sparse and cold start issues, which cause overfitting problems in previous neural network and matrix factorization (MF) models. Additionally, the interpersonal relations, evaluation text and existing “user-item” formatted rating matrix constitute a multi-source and multi-modal data structure, so a systematic data fusion method is needed to establish recommendations based on these heterogeneous characteristics. Therefore, a hybrid recommendation model by fusing network structured feature with graph neural networks and user interactive activities with tensor factorization was proposed in this paper. First, a graph structured teaching evaluation network is proposed to describe students, courses, and other entities by using the students’ rating, commentary text, grading and interpersonal relations. Then, a random walk based neural network is employed to generate the vectorized representation of students by learning their own relational structure. Finally, by recognizing these personalization features as the third dimension of the rating tensor, a Bayesian Probabilistic Tensor Factorization-based tensor factorization is applied to learn and predict students’ ratings for classes they have not taken. Experiments on a real-world evaluation of teaching system including 532 participants with 7,453 rating records show that the proposed method outperforms other existing neural network and matrix factorization models including xSVD++, RTTF and DSE with a smaller predictive error as well as better recommendation accuracy.