What is candidate generation in large-scale recommender systems?

Candidate generation retrieves a small subset of relevant items from a large catalog.

Instead of scoring millions of items, systems first narrow down to a few hundred or thousand candidates using embeddings, approximate nearest neighbors, or heuristic filters. This reduces latency and enables real-time recommendations.