Calculator Inputs
Enter one rank per query. Use commas, spaces, or new lines. Use 0 when the system fails to return any relevant result.
Example Data Table
This sample shows how reciprocal rank is assigned for each query before averaging them into one final MRR score.
| Query | First Relevant Rank | Reciprocal Formula | Reciprocal Rank |
|---|---|---|---|
| Question Answering | 1 | 1/1 | 1.0000 |
| Document Search | 2 | 1/2 | 0.5000 |
| Image Retrieval | 0 | 0 | 0.0000 |
| Code Search | 4 | 1/4 | 0.2500 |
| FAQ Matching | 3 | 1/3 | 0.3333 |
| Example Mean Reciprocal Rank | 0.4167 | ||
Formula Used
Mean Reciprocal Rank measures how early the first relevant result appears for each query. Higher values mean better ranking quality.
MRR = (1 / |Q|) × Σ (1 / ranki)
- |Q| = total number of queries.
- ranki = position of the first relevant result for query i.
- If no relevant result exists, reciprocal rank becomes 0.
- The final MRR is the average of all reciprocal ranks.
How to Use This Calculator
- Enter the first relevant rank for each evaluated query.
- Use 0 for any query with no relevant result.
- Optionally add query labels, report name, chart type, and sort preference.
- Click Calculate MRR to show the results above the form.
- Review MRR, hit rate, top-k rates, graph patterns, and per-query details.
- Export the output as CSV or PDF for reporting and sharing.
FAQs
1) What does mean reciprocal rank measure?
It measures how high the first relevant result appears across many queries. Higher MRR means relevant answers usually appear earlier, which signals stronger ranking performance for retrieval, search, recommendation, and question answering systems.
2) What does a rank of 0 mean here?
A zero means the system did not return any relevant result for that query. Its reciprocal rank becomes zero, which lowers the final average and exposes failure cases clearly.
3) Is a higher MRR always better?
Yes. A larger MRR means relevant answers appear earlier on average. A value close to 1 is excellent, while lower values suggest users may need to scroll deeper before finding useful results.
4) Can I use decimals for ranks?
Ranks normally represent integer positions in a ranked list, so whole numbers are best. This calculator rounds positive numeric inputs to the nearest whole rank before calculating reciprocal values.
5) When should I use MRR instead of accuracy?
Use MRR when rank position matters. Accuracy only checks whether a result is correct, while MRR rewards systems that place the first relevant answer closer to the top.
6) Why include top-1, top-3, and top-10 rates?
These rates add practical context. MRR gives one summary score, but top-k rates show how often users likely find relevance within shallow browsing depth, which is useful for product evaluation.
7) Can this help compare two retrieval models?
Yes. Run one evaluation set for each model using the same queries. Compare the resulting MRR, hit rate, and graph patterns to see which model ranks relevant items earlier.
8) Is MRR enough for a full evaluation?
Not always. MRR is excellent for first-hit quality, but complete evaluation often also includes precision, recall, NDCG, latency, and coverage, depending on your system goals.