Design a Recommendation System (Netflix)

To design Netflix's recommendation engine, I first clarify that our primary goal is maximizing engagement while solving the cold-start problem for new content. We need to support millions of concurrent users with sub-second latency. Architecturally, I propose a two-stage funnel: a retrieval layer using approximate nearest neighbors to narrow down candidates, followed by a ranking layer using deep learning models. For algorithms, I would implement a hybrid approach. Collaborative Filtering, specifically matrix factorization or neural collaborative filtering, captures user-item interactions effectively. However, to handle new movies lacking interaction history, we integrate Content-Based filtering using metadata like genre, cast, and plot embeddings. The training pipeline involves ingesting clickstream data via Kafka, processing it with Spark for feature extraction, and training models daily on distributed clusters. Crucially, we must deploy models with an A/B testing framework to validate improvements before global rollout. Finally, continuous monitoring ensures we detect data drift in user preferences, triggering automatic retraining pipelines to maintain accuracy over time.