holisticai.bias.metrics.avg_recommendation_popularity

holisticai.bias.metrics.avg_recommendation_popularity(mat_pred, top=None, thresh=0.5, normalize=False)

Average Recommendation Popularity

This function computes the average recommendation popularity of items over users. We define the recommendation popularity as the average amount of times an item is recommended.

Interpretation

A low value is desidered and suggests that items have been recommended equally across the population.

Parameters

mat_predmatrix-like

Matrix with shape (num_users, num_items). A recommender score (binary or soft pred) for each user,item interaction.

topint, optional

If not None, the number of items that are shown to each user.

threshfloat, optional

Threshold indicating value at which a given item is shown to user (if top is None).

normalizebool, optional

If True, normalises the data matrix to [0,1] range.

Returns

float

Average Recommendation Popularity

References

[1]

M Mansoury, H Abdollahpouri, M Pechenizkiy, B Mobasher, R Burke, E Malthouse (2020). User-centered Evaluation of Popularity Bias in Recommender Systems, <https://arxiv.org/pdf/2103.06364.pdf>

Examples

>>> import numpy as np
>>> from holisticai.bias.metrics import avg_recommendation_popularity
>>> mat_pred = np.array([[0.9, 0.8, 0.4, 0.2],
                      [0.7, 0.9, 0.1, 0.7],
                      [0.3, 0.2, 0.3, 0.3],
                      [0.2, 0.1, 0.7, 0.8],
                      [0.8, 0.7, 0.9, 0.1],
                      [1. , 0.9, 0.3, 0.6],
                      [0.8, 0.9, 0.1, 0.1],
                      [0.2, 0.3, 0.1, 0.5],
                      [0.1, 0.2, 0.7, 0.7],
                      [0.2, 0.7, 0.1, 0.2]])
>>> avg_recommendation_popularity(mat_pred, top=None, thresh=0.5, normalize=False)
5.037037037037036