What is Reciprocal rank fusion?
Verified 5 August 2026
The problem with combining a BM25 ranking and a vector ranking is that their scores are not comparable — one is an unbounded relevance score, the other a bounded similarity. Any weighted sum requires normalisation that has to be tuned per corpus and quietly stops working when the corpus changes.
Reciprocal rank fusion ignores the scores entirely and uses positions. A document ranked third in one list and eighth in the other scores 1/(k+3) + 1/(k+8), where k is a smoothing constant conventionally set to 60. No tuning, no normalisation, and it works across arbitrary numbers of lists.
Common questions
What is Reciprocal rank fusion?
Reciprocal rank fusion merges several ranked lists by scoring each document by the reciprocal of its position in each list, requiring no score normalisation.
How does Reciprocal rank fusion work in practice?
Reciprocal rank fusion ignores the scores entirely and uses positions. A document ranked third in one list and eighth in the other scores 1/(k+3) + 1/(k+8), where k is a smoothing constant conventionally set to 60. No tuning, no normalisation, and it works across arbitrary numbers of lists.
See it working
The free tier is 10,000 requests a month with no card — enough to test any of this against your own queries.