This research paper introduces Monolith, a real-time recommendation system designed by Bytedance. Addressing limitations of existing deep learning frameworks, Monolith uses a novel collisionless embedding table to efficiently handle sparse, dynamic features, significantly improving model quality and memory usage. A key innovation is its online training architecture, enabling real-time model updates based on user feedback. The authors demonstrate Monolith’s superior performance through experiments and A/B tests, highlighting the trade-offs between real-time learning and system reliability. Finally, the paper compares Monolith to existing solutions, showcasing its advantages in scalability and efficiency for large-scale recommendation tasks.