Relative Score Fusion
A hybrid search fusion strategy that normalizes scores from multiple retrieval systems to a common scale and combines them linearly, preserving score magnitude information that Reciprocal Rank Fusion (RRF) discards.
Part of the “classic hybrid search techniques” alongside RRF, as described in Erik Hatcher’s MongoDB hybrid search series.
How It Works
- Retrieve results from each retrieval system (e.g., BM25, dense vector)
- Normalize each system’s scores to [0, 1] using min-max or other normalization
- Combine normalized scores:
final = α × score_lexical + (1 − α) × score_dense
RRF vs RSF
| Property | RRF | RSF |
|---|---|---|
| Uses score values | No (rank only) | Yes |
| Score normalization required | No | Yes |
| Sensitive to score outliers | No | Yes |
| Preserves score magnitude | No | Yes |
| Parameter-free | Yes (except k) | No (requires α) |
Limitations
- Requires score normalization across systems with incompatible scales
- Score distributions can differ per query, making normalization unstable
- Needs α weight tuning, unlike RRF which is robust out of the box
Related Concepts
- Reciprocal Rank Fusion — rank-based alternative; simpler, more robust
- Hybrid Search — the context where RSF is applied
- Linear Score Combination — closely related approach
- Semantic Boosting — alternative two-phase approach; avoids list merging entirely
Articles
- Hybrid Search Blueprint Series Semantic Boosting — series that covers RSF alongside RRF and Semantic Boosting