Graph Neural Network Collaborative Filtering Recommendation with Integrated Attention Mechanism and Contrastive Learning

Graph Neural Network (GNN)-based collaborative filtering has emerged as a powerful approach for recommendation systems by leveraging user-item interaction graphs to capture higher-order relationships. While existing methods exploit graph topology, they often overlook the heterogeneous importance of neighboring nodes and suffer from data sparsity, leading to sub optimal representations. To address these issues, we propose a novel GNN-based model that synergistically integrates efficient simple dot-product attention with multi-view contrastive learning. Specifically, the attention module assigns context-aware weights to neighbors based on spectral graph theory, dynamically filtering noisy interactions. Concurrently, structural contrastive learning (via edge dropout) and semantic contrastive learning (via feature augmentation) generate hard negative samples to enhance representation discriminability. The unified objective function combines Bayesian Personalized Ranking loss with contrastive losses, jointly optimizing recommendation accuracy and robustness. Extensive experiments on three benchmarks (MovieLens-1M, Yelp, and Amazon-Books) demonstrate consistent improvements of $\mathbf{1 2. 3 \%}$ in Recall@20 and 9.7% in NDCG@20 over state-of-the-art baselines, particularly under sparse interactions. Ablation studies further validate the necessity of contrastive components.