lenskit.metrics.ranking.RankBiasedEntropy#

class lenskit.metrics.ranking.RankBiasedEntropy(dataset, attribute, n=None, *, weight=None)#

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

Evaluate diversity using rank-biased Shannon entropy over item categories.

This metric measures the diversity of categories in recommendation list with rank-based weighting, giving more importance to items at the top of the recommendation list.

Parameters:

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

  • attribute (str) – Name of the attribute to use for categories (e.g., ‘genre’, ‘tag’)

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

  • weight (lenskit.metrics.ranking._weighting.RankWeight | None) – Rank weighting model. Defaults to GeometricRankWeight(0.85)

Stability:
Caller (see Stability Levels).
attribute: str#
weight: lenskit.metrics.ranking._weighting.RankWeight#
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