From theory to practice: The evolution and comparative analysis of homogeneous vs. heterogeneous Graph Neural Networks in recommender systems
Maryam Khanian Najafabadi, Rei-An Chen, Javad Rezazadeh, Amin Beheshti, Nasrin Shabani
Abstract
Graph Neural Networks (GNNs) have emerged as powerful tools for recommendation systems, addressing challenges such as complex user-item relationships and dynamic behaviors. This paper provides a concise review of GNN applications, focusing on embedding techniques and state-of-the-art algorithms. We evaluate three GNN architectures: Homogeneous GNN, Heterogeneous GNN, and a Heterogeneous GNN enhanced with Skip-Gram node embeddings. These models are assessed on subsets of the Amazon 2023 dataset, covering Fashion, Beauty, and Musical Instruments, using metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Our findings highlight the superior performance of GNNs in capturing nuanced user-item interactions, improving recommendation accuracy, scalability, and adaptability. The integration of Skip-Gram embeddings further enhances item similarity modeling, enabling more personalized recommendations. We also analyze the computational efficiency of these models, offering insights for their deployment in large-scale systems. This study bridges the gap between theoretical advancements and practical applications of GNNs in recommendation systems. By synthesizing recent trends, identifying research gaps, and presenting actionable insights, it serves as a foundational reference for researchers and practitioners aiming to optimize GNN-based models for diverse scenarios. The results underscore the transformative potential of GNNs in delivering accurate, scalable, and real-time recommendations, setting the stage for future innovations in this domain.