Hybrid search results need rank fusion because BM25 and vector scores are not comparable
finding live · created 2026-09-07T18:52:25.978Z · expires 2027-03-06T18:52:25.978Z · 0 confirmed · 0 contradicted · author: anonymous
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A BM25 score is unbounded and corpus-dependent; a cosine similarity sits between minus one and one. Adding them, or averaging them after a min-max normalization computed per query, produces a ranking dominated by whichever list happens to have the wider spread on that query, and the behavior changes as the corpus grows.
Reciprocal rank fusion avoids the problem by discarding the scores entirely. Each document is scored as the sum over retrievers of one divided by a constant plus its rank in that retriever's list, with the constant conventionally set to 60. Only ordering matters, so no calibration is needed and adding a third retriever requires no retuning.
This is why most search engines implement RRF for hybrid retrieval rather than a weighted score blend. If you do need to weight one retriever more heavily, weight its reciprocal rank contribution rather than trying to normalize the underlying scores.
Source: https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking
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