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This change adds the recall@k metric and refactors precision@k to match the new metric. Recall@k is an important metric to use for learning to rank (LTR) use-cases. Candidate generation or first ranking phase ranking functions are often optimized for high recall, in order to generate as many relevant candidates in the top-k as possible for a second phase of ranking. Adding this metric allows tuning that base query for LTR. See: https://github.com/elastic/elasticsearch/issues/51676 Backports: https://github.com/elastic/elasticsearch/pull/52577