Netflix’s recommendation platform processes terabytes of user interaction data daily to generate real-time, individualized recommendations. The system integrates behavioral signals such as viewing duration, skip behavior, and contextual factors like device and time of day. Using distributed pipelines and edge-based serving, it achieves sub-100ms latency at global scale.

The architecture employs ensemble learning that combines matrix factorization, deep neural networks, and graph neural networks (SemanticGNN). Matrix factorization provides interpretable latent features for users and content, deep models capture nonlinear viewing patterns and temporal sequences, and GNNs create semantic relationships between titles to solve cold-start problems. Contextual bandits dynamically optimize interface elements like artwork and homepage layout for engagement, while multi-task learning (Hydra) consolidates ranking, search, and notifications for efficiency.

Netflix’s ML infrastructure runs training on AWS using Spark and TensorFlow while serving occurs via its Open Connect CDN for low latency. Sophisticated MLOps ensures continuous model improvement with automated A/B testing, drift detection, and canary deployments. Causal inference and reinforcement learning techniques optimize for long-term satisfaction over short-term clicks, creating sustained business value through churn reduction and improved retention. This system represents a benchmark for scalable, data-driven personalization that continuously learns from billions of interactions.