AI engineering glossary · Evaluation metrics
What is ERR (expected reciprocal rank)?
Expected reciprocal rank (also called ERR): A ranking metric built on a cascade user model: the reader scans down the list and stops once satisfied, with the chance of satisfaction growing with an item's relevance grade. The score is the expected reciprocal of the position where the user stops. It rewards a strong result early and discounts redundancy.
Expected reciprocal rank: an example
If a very relevant page sits at rank 1, most simulated users stop there, so later pages add little to the ERR score.
Related terms
- Normalised discounted cumulative gain
- Reciprocal rank
- Rank-biased precision
- Mean reciprocal rank
More Evaluation metrics terms
- Explained variance score
- F-beta score
- F1 score
- False negative
- False negative rate
- False positive
- False positive rate
- Fleiss' kappa
Where you learn it
Expected reciprocal rank is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.