Recall@K, Precision@K, MRR, and nDCG
Recall@K measures relevant evidence found within the top K; Precision@K measures the share of top K that is relevant; MRR rewards early placement of the first relevant result; nDCG supports graded relevance and ranking quality. No single metric is enough. Use different metrics for coverage and noise, and inspect examples manually.
Implementation example
function recallAtK(ids,relevant,k) {
const set=new Set(relevant);
if(!set.size) return null;
return ids.slice(0,k).filter(id=>set.has(id)).length/set.size;
}💡 This simple implementation assumes IDs and relevance labels are correct.
Key Takeaways
- Inspect the relevant pipeline stage instead of guessing.
- Preserve source provenance, versions, and access scope.
- Measure quality on representative queries.
Knowledge Check
Which metric asks whether relevant items appear within top K?