Entertainment recommendation systems represent some of the most sophisticated AI deployments at scale. Netflix: two-stage retrieval + ranking, candidate generation using matrix factorization on viewing history, ranking using gradient boosted trees with 100+ features including day/time, device, social signals. Netflix Research estimates 80% of content discovery is through recommendations. Spotify Discover Weekly: collaborative filtering on listening sequences using Word2Vec-style embeddings (track2vec), acoustic feature similarity using audio CNNs, playlist continuation models. Real-time personalization: contextual bandits balancing exploration (new content) vs exploitation (known preferences) - addresses cold start problem for new releases. TikTok For You Page: pure machine learning with minimal manual curation - multi-task learning predicting play time, likes, shares, follows simultaneously. Content understanding: multimodal models analyzing video, audio, text, and user interaction patterns. A/B testing infrastructure: continuous experimentation with holdout groups measuring long-term retention impact vs short-term engagement. Diversity enforcement: preventing filter bubbles through explicit diversity objectives in ranking.