Evaluating Top-N Rankings#

The lenskit.metrics.ranking module contains the core top-N ranking accuracy metrics (including rank-oblivious list metrics like precision, recall, and hit rate).

Ranking metrics extend the RankingMetricBase base class, often in addition to ListMetric, and return a score given a recommendation list and a test rating list, both as item lists; most metrics require the recommendation item list to be ordered.

All LensKit ranking metrics take n as a constructor argument to control the list of the length that is considered; this allows multiple measurements (e.g. HR@5 and HR@10) to be computed from a single set of rankings.

Changed in version 2026.1: The argument for the list length has changed from k to n, for consistency across LensKit. k is kept as a deprecated alias until 2027.1.

Changed in version 2025.1: The top-N accuracy metric interface has changed to use item lists, and to be simpler to implement.

Included Effectiveness Metrics#

List and Set Metrics#

These metrics just look at the recommendation list and do not consider the rank positions of items within it.

Hit

Compute whether or not a list is a hit; any list with at least one

Precision

Compute recommendation precision. This is computed as:

Recall

Compute recommendation recall. This is computed as:

Ranked List Metrics#

These metrics treat the recommendation list as a ranked list of items that may or may not be relevant; some also support different item utilities (e.g. ratings or graded relevance scores).

RecipRank

Compute the reciprocal rank [KV97] of the first relevant

RBP

Evaluate recommendations with rank-biased precision [MZ08].

NDCG

Compute the normalized discounted cumulative gain [JarvelinKekalainen02].

DCG

Compute the _unnormalized_ discounted cumulative gain [JarvelinKekalainen02].

Beyond Accuracy#

These metrics measure non-accuracy properties of recommendation lists, such as popularity/obscurity or diversity.

MeanPopRank

Compute the _obscurity_ (mean popularity rank) of the recommendations.