Large Scale Recommendation System

Large-scale recommendation systems aim to provide personalized recommendations efficiently to vast numbers of users, focusing on improving accuracy, speed, and fairness. Current research emphasizes developing more efficient model architectures, such as hierarchical models and those leveraging hardware acceleration (e.g., through techniques like Flash Attention), to handle extremely long user interaction sequences and high-cardinality features. These advancements are crucial for improving the performance and scalability of recommendation systems in real-world applications, impacting areas like e-commerce, online advertising, and content streaming.