lenskit.metrics.RBP#

class lenskit.metrics.RBP(k=None, *, patience=0.5, normalize=False)#

Bases: ListMetric, RankingMetricBase

Evaluate recommendations with rank-biased precision [MZ08] with a patience parameter \(\gamma\).

If \(r_{ui} \in \{0, 1\}\) is binary implicit ratings, this is computed by:

\[\begin{align*} \operatorname{RBP}_\gamma(L, u) & =(1 - \gamma) \sum_i r_{ui} p^i \end{align*}\]

The original RBP metric depends on the idea that the rank-biased sum of binary relevance scores in an infinitely-long, perfectly-precise list has is \(1/(1 - \gamma)\). However, in recommender evaluation, we usually have a small test set, so the maximum achievable RBP is significantly less, and is a function of the number of test items. With normalize=True, the RBP metric will be normalized by the maximum achievable with the provided test data.

Parameters:

  • k (int | None) – The maximum recommendation list length.

  • patience (float) – The patience parameter \(\gamma\), the probability that the user continues browsing at each point.

  • normalize (bool) – Whether to normalize the RBP scores; if True, divides the RBP score by the maximum achievable with the test data (as in nDCG).

__init__(k=None, *, patience=0.5, normalize=False)#
Parameters:

Methods

__init__([k, patience, normalize])

measure_list(recs, test)

Compute the metric value for a single result list.

truncate(items)

Truncate an item list if it is longer than k.

Attributes

default

The default value to infer when computing statistics over missing values.

k

The maximum length of rankings to consider.

label

The metric's default label in output.

patience

normalize

property label#

The metric’s default label in output.

The base implementation returns the class name by default.

measure_list(recs, test)#

Compute the metric value for a single result list.

Individual metric classes need to implement this method.

Parameters:
Return type:

float