lenskit.metrics.ranking.ILS#

class lenskit.metrics.ranking.ILS(dataset, attribute, n=None)#

Bases: lenskit.metrics.ranking._base.ListMetric, lenskit.metrics.ranking._base.RankingMetricBase

Evaluate recommendation diversity using intra-list similarity (ILS).

This metric measures the average pairwise cosine similarity between item vectors in a recommendation list. Lower values indicate more diverse recommendations, while higher values indicate less diverse recommendations.

Parameters:

  • dataset (lenskit.data.Dataset) – The LensKit dataset containing item entities and their attributes.

  • attribute (str) – Name of the attribute or vector source (e.g., ‘genre’, ‘tag’).

  • n (int | None) – Recommendation list length to evaluate.

Stability:
Caller (see Stability Levels).
attribute: str#
property label#

The metric’s default label in output. The base implementation returns the class name by default.

measure_list(recs, test)#

Compute measurements for a single list.

Returns:

  • A float for simple metrics

  • Intermediate data for decomposed metrics

  • A dict mapping metric names to values for multi-metric classes

Parameters:
Return type:

float