Rank fusion versus score fusion
Reciprocal Rank Fusion, or RRF, combines results primarily from their ranks. A candidate receives a contribution based on its position in each result list, so the absolute score values do not need to be comparable across retrieval systems.
Distribution-Based Score Fusion, or DBSF, instead uses the score distributions of the result sets to normalize their scores before combining them. That allows score magnitude to contribute, but it also means the result distribution can influence the final ranking.
The trade-off is robustness versus score information. RRF is attractive when dense and sparse systems produce incompatible score scales or when you want a simple rank-based ensemble. DBSF can be useful when relative score distributions contain meaningful information and the result sets have enough statistical stability.
A common mistake is treating a larger raw score as inherently comparable across prefetches. Dense cosine scores, sparse scores, and other ranking functions can have very different scales. Fusion should explicitly account for that. Exact fusion behavior and parameter details should be checked against the Qdrant version in use.
RRF is based on result rank and does not require comparable raw scores
DBSF uses normalized score distributions
RRF is often robust for heterogeneous retrieval signals
Fusion choice should be evaluated on representative relevance data
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