Minju Noh  Hyun Ji Jeong

10.5626/JOK.2026.53.2.139

Graph Neural Network Self-Supervised Learning Recommendation System diffusionmodel

Recently, graph augmentation methods utilizing random dropout have been introduced in graph learning-based recommender systems. However, these methods can dimish recommendation performance by losing crucial user-item interaction signals. To address this issue, we propose a self-supervised recommendation model that leverages diffusion model to generate new user-item interactions. This approach maintains meaningful target behavior signals while effectively reflecting user preferences. Furthermore, we integrate multi-behavior learning to capture informative signals from auxiliary behaviors and mitigate data sparsity. Our experiments on two real-world e-commerce datasets demonstrate that our proposed model outperforms previous studies that generate augmented graphs through random edge dropping, as measured by Hit@10 and Recall@10. This indicates that interaction augmentation via diffusion models can amplify important signals and enhance recommendation performance.