Recommendation Systems

Recommendation systems power the algorithms that determine what content, products, and information billions of people see across the internet, from Netflix's movie suggestions and Spotify's playlists to Amazon's product recommendations and TikTok's For You Page. The technology underlying recommendation systems has evolved from collaborative filtering (users who liked X also liked Y) through content-based filtering (analyzing item attributes) to modern deep learning approaches that combine multiple signal types including user behavior, item features, contextual information, and increasingly, LLM-powered understanding of user intent. Recommendation systems are among the most commercially valuable AI applications: Amazon attributes 35%+ of its revenue to product recommendations, Netflix estimates its recommendation system saves $1 billion annually in reduced churn, and TikTok's algorithm is widely considered the single most important factor in its explosive global growth. The sector serves both platform companies (building their own systems) and enterprise companies (deploying recommendation technology in e-commerce, media, and content applications).