Decentralized Recommender System

Decentralized recommender systems aim to improve privacy and scalability in recommendation systems by distributing computation and data storage across multiple nodes, rather than relying on a central server. Current research focuses on efficient aggregation techniques for model parameters or raw data, employing architectures like transformers and matrix factorization, and exploring various decentralized optimization algorithms including gossip learning and differentially private methods to address privacy concerns. This approach offers significant advantages in terms of data security, robustness to server failures, and potential for handling massive datasets, impacting both the development of privacy-preserving machine learning and the deployment of large-scale recommendation systems.