Enhanced Self-supervised Graph Learning Algorithm for Social Recommendation

Graph neural networks (GNNs) have achieved success in social recommender systems due to its capacity of capturing high-order collaborative filtering effects. However, some issues, such as vector scale distortion and over-smoothing, are obstacles to improving the performance of GNN-based models. This paper investigates these two issues and proposes an enhanced self-supervised graph learning (ESGL) algorithm based on GNN for social recommendation. By introducing a scale regulation module, ESGL can solve the issue of vector scale distortion. The scale regulation module takes the output of GNN as its input and generates the scaling matrix that limits the scale of embeddings. In addition, ESGL adopts two kinds of self-supervised learning schemes to enhance the recommendation performance. One scheme is to learn the similarity between users, while the other scheme is the embedding-level contrastive learning. The latter regards the initial normalized embedding of a user node and its average normalized embedding generated in the rest layers as positive samples. Experimental results have shown that our model is effective in improving recommendations.