Candidate generation is the first stage in a recommendation or retrieval system that selects a manageable set of potentially relevant items from a large corpus. Later stages, such as scoring, ranking, or re-ranking, evaluate this smaller candidate set more carefully to produce the final recommendations.
The initial set of recommendations chosen by a recommendation system. For example, consider a bookstore that offers 100,000 titles. The candidate generation phase creates a much smaller list of suitable books for a particular user, say 500. But even 500 books is way too many to recommend to a user. Subsequent, more expensive, phases of a recommendation system (such as scoring and re-ranking) reduce those 500 to a much smaller, more useful set of recommendations. See Candidate generation overview in the Recommendation Systems course for more information.