  While recommender systems have become an integral component of the Web
experience, their heavy reliance on user data raises privacy and security
concerns. Substituting user data with synthetic data can address these
concerns, but accurately replicating these real-world datasets has been a
notoriously challenging problem. Recent advancements in generative AI have
demonstrated the impressive capabilities of diffusion models in generating
realistic data across various domains. In this work we introduce a Score-based
Diffusion Recommendation Module (SDRM), which captures the intricate patterns
of real-world datasets required for training highly accurate recommender
systems. SDRM allows for the generation of synthetic data that can replace
existing datasets to preserve user privacy, or augment existing datasets to
address excessive data sparsity. Our method outperforms competing baselines
such as generative adversarial networks, variational autoencoders, and recently
proposed diffusion models in synthesizing various datasets to replace or
augment the original data by an average improvement of 4.30% in Recall@k and
4.65% in NDCG@k.
