Large Scale Recommendation

Large-scale recommendation systems aim to efficiently provide personalized recommendations to vast numbers of users from massive item catalogs. Current research focuses on improving the scalability and accuracy of these systems, exploring architectures like deep learning recommendation models (DLRMs), graph neural networks (GNNs), and factorization machines, often incorporating techniques like processing-in-memory (PIM) hardware acceleration and efficient negative sampling strategies. These advancements are crucial for enhancing user experience in various applications, from e-commerce and streaming services to online advertising, and are driving significant improvements in recommendation quality and system efficiency.