Publication Date
4-12-2024
Document Type
Article
Publication Title
IEEE Access
Volume
12
DOI
10.1109/ACCESS.2024.3388299
First Page
58275
Last Page
58287
Abstract
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 .
Keywords
Training, Recommender systems, Data models, Synthetic data, Data privacy, Noise reduction, Gaussian distribution, Diffusion processes, Machine learning
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Computer Engineering
Recommended Citation
Derek Lilienthal, Paul Mello, Magdalini Eirinaki, and Stas Tiomkin. "Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems" IEEE Access (2024): 58275-58287. https://doi.org/10.1109/ACCESS.2024.3388299
