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 in addition to ListMetric and/or GlobalMetric, 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 k 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 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 relevant item in the first \(k\) positions (\(L_{\le k} \cap I_u^{\mathrm{test}} \ne \emptyset\)) is scored as 1, and lists with no relevant items as 0.
Precision	Compute recommendation precision.
Recall	Compute recommendation recall.

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 item in a list of recommendations.
RBP	Evaluate recommendations with rank-biased precision [MZ08] with a patience parameter \(\gamma\).
NDCG	Compute the normalized discounted cumulative gain [JarvelinKekalainen02].