Course Recommendation Model Based on Layer Dropout Graph Differential Contrastive Learning

At present, the course recommendation model of graph collaborative filtering mainly uses bipartite graph modeling to obtain user-course cooperative relationship. However, the bipartite graph lacks the acquisition of user-user and course-course relationship information. In addition, due to the inherent defects of graph convolution, multi-layer graph convolution will cause overfitting problems. Moreover, the existing graph contrastive learning methods to solve the sparsity of recommendation data simply divide nodes into positive and negative pairs, without taking into account that users who have chosen the same course in the recommendation are similar. In contrastive learning, the feature similarity distance of these users should be different.To solve these problems, a course recommendation model based on layer dropout graph differential contrastive learning(DGDCL) is proposed. Specifically, a hybrid graph convolution network of fusion graph and hypergraph is used to obtain both low-order and high-order information. Then, using the layer dropout method to alleviate overfitting in neural network, the multi-layer feature embeddings of graph nodes are randomly dropout. Finally, two different layer drops are used to generate the contrastive views to reduce the additional noise and computational overhead of generating the contrastive views. The prior similarity of users and courses is used to adjust the calculation of the contrastive loss function, and differentiated contrastive learning of graph nodes is realized to make the contrastive learning more suitable for the recommendation model. The experimental results of XuetangX and MOOCCube datasets show that the proposed model is better than the existing model.