Research on recommendation system algorithm based on graph neural network

This paper focuses on the algorithm research of recommendation system based on Graph Neural Networks (GNNs), aiming to build a more accurate and personalized recommendation model by integrating multi-source information suchas user behavior data and item attributes. First, this paper reviews the basic principles of GNN and its advantages in dealing with complex network structures, especially its ability to capture deep associations between nodes. Then, several typical GNN-based recommendation system models are discussed in detail, including but not limited to GCN (Graph Convolutional Networks), GAT (Graph Attention Networks), etc. The performance and limitations of these models in different application scenarios are analyzed. In addition, this paper also proposes an improved GNN model, which further enhances the modeling ability of user interests and item features by introducing multi-order features and multi-layer perceptrons. Experimental results show that the proposed model has higher accuracy than traditional recommendation algorithms on multiple public data sets.