Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems. (arXiv:2311.03488v2 [cs.IR] UPDATED)

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 …

address data datasets experience generative generative ai privacy privacy and security problem real recommender systems resolution security security concerns sensitive synthetic synthetic data systems the web user data web world