Collaborative Recommendation Based on Graph Neural Network Clustering Subgraphs Using Dual Graph Attention Network

A recommendation system based on Graph Neural Network (GNN) can extract high-order connectivity between users and items. Collaborative Filtering (CF) is a classic recommendation algorithm that suffers from over-smoothing issues during the stacking of multilayer graph convolutional layers owing to the similarity between user and item embeddings. To address this issue, a graph neural network collaborative filtering recommendation algorithm named DAC-GCN that generates subgraphs using a dual graph attention mechanism is proposed. Users with common interests are clustered to generate subgraphs to avoid spreading negative information from high-order neighbors to the embedding learning. The graph attention mechanism is used in advance to preprocess node embeddings, increasing attention to important nodes and improving subgraph generation results. In addition, the graph attention mechanism is reintroduced during the subgraph propagation process to enhance the node discrimination within the subgraph, thereby improving the propagation of embedded information within the subgraph, reducing the impact of over-smoothing, and enhancing the recommendation performance. Finally, the proposed algorithm is tested on three publicly available datasets using Normalized Discounted Cumulative Gain (NDCG) and recall as evaluation metrics. The experimental results validate the effectiveness and superiority of the proposed algorithm.