How a Recommendation System Actually Works: Candidate Generation and Ranking

You cannot score ten million videos for every user on every page load. That single constraint explains almost the entire architecture of a modern recommendation system. The solution is a funnel: cheaply narrow ten million items down to a few hundred, then spend real compute ranking only those. The core problem A good recommendation is personal and fresh, which means it has to be computed close to request time. But a heavy ranking model that considers hundreds of features might take a…