Applications and Future of Dense Retrieval in Industry
Yubin Kim · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Large-scale search engines are often designed as tiered systems with at least two layers. The L1 candidate retrieval layer efficiently generates a subset of potentially relevant documents (typically ~1000 documents) from a corpus many orders of magnitude larger in size. L1 systems emphasize efficiency and are designed to maximize recall. The L2 re-ranking layer uses a more computationally expensive, but more accurate model (e.g. learning-to-rank or neural model) to re-rank the candidates generated by L1 in order to maximize precision of the final result list.