Recommendation systems predict what users will like using a combination of content-based and collaborative filtering. In production, they follow a two-step approach: retrieval to narrow down candidates and ranking to personalize results. The system relies heavily on user interaction data, and success is measured using engagement metrics like CTR and watch time rather than accuracy.
