Hybrid FM-GCN-Attention Model for Personalized Recommendation

Personalized recommendation systems play a crucial role in enhancing user engagement and decision-making across various domains. Traditional approaches, such as collaborative filtering and matrix factorization, have shown effectiveness but suffer from data sparsity and cold-start problems. Recent advances in deep learning, graph-based models, and attention mechanisms have significantly improved recommendation performance. This paper proposes a novel hybrid recommendation model that integrates Factorization Machines (FM), Graph Convolutional Networks (GCN), and Multi-Layer Attention Networks (MLAN) to optimize feature representations and enhance prediction accuracy. Experimental results demonstrate the superiority of the proposed approach over baseline methods in key performance metrics.