Recommendations: suggest items users might like. Approaches: collaborative filtering (users who liked X also liked Y), content-based (similar item attributes), hybrid.
Pipeline: candidate generation (millions → thousands) then ranking (thousands → dozens). Features: user history, item attributes, context. Offline training on historical data, online inference in real-time. A/B test recommendation algorithms continuously.