GCN-SA: a hybrid recommendation model based on graph convolutional network with embedding splicing layer

Graph convolutional networks are capable of handling non-Euclidean data with sparse features, and some research has begun to apply them to the field of recommendation systems. Graph convolutional network’s aggregation and propagation mechanism can learn features well and improve the embedding quality. However, simply applying GCN to the recommendation domain can only show some of its advantages, and the complex structure makes it difficult for the model to handle the massive amount of data in the industrial domain. Some work has been done to integrate GCN with recommendation systems better. However, most related work pursues the model’s simplicity and ignores the large amount of hidden auxiliary information. In this paper, we propose a GCN-SA model, which adds a multi-head self-attention mechanism to the aggregation and propagation process to learn the weights of neighboring nodes and analyze the importance of the relationships between nodes; we also design a new embedding splicing layer for the graph convolutional network, which dynamically adjusts the embedding of different layers to achieve adaptive layer smoothing and mitigate the over-smoothing phenomenon. After experimental results on five benchmark datasets, we show that the GCN-SA model outperforms previous related work, captures a large amount of auxiliary information, and enhances the expressive ability of the model.