Deep Learning Recommendation Model

Deep learning recommendation models (DLRMs) aim to create highly personalized recommendations by leveraging deep learning techniques on massive datasets of user interactions. Current research focuses on optimizing DLRM training and inference efficiency through techniques like lossy compression, processing-in-memory architectures, and novel sharding strategies to address the computational and memory bottlenecks posed by large-scale models. These advancements are crucial for improving the scalability, speed, and cost-effectiveness of recommendation systems across various applications, impacting fields from e-commerce to online advertising.