What is candidate generation and what approaches are there (BM25, dense, hybrid)?

Candidate generation is a stage in search and recommendation systems where a limited set of relevant candidates is selected from a large volume of data for further, more precise processing (e.g., ranking).

Main approaches to candidate generation:

• BM25 — a classic method based on inverse document frequency (TF-IDF), which evaluates document relevance based on query keywords. It works well with textual data, is fast, and interpretable.

• Dense retrieval — uses dense vector representations (embeddings) of queries and documents obtained through neural networks. It allows considering semantics and context, not just exact word matches.

• Hybrid — combines BM25 and dense approaches to leverage the advantages of both: exact keyword matching and semantic understanding. For example, candidates are initially selected with BM25, then expanded using dense retrieval.

This approach effectively narrows down the set of documents to relevant candidates for subsequent, more in-depth analysis and ranking.