elliot.evaluation.metrics.accuracy.mrr package

Submodules

elliot.evaluation.metrics.accuracy.mrr.mrr module

This is the implementation of the Mean Reciprocal Rank metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.accuracy.mrr.mrr.MRR(recommendations, config, params, eval_objects)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

Mean Reciprocal Rank

This class represents the implementation of the Mean Reciprocal Rank recommendation metric. Passing ‘MRR’ to the metrics list will enable the computation of the metric.

For further details, please refer to the link

\[\mathrm {MRR} = \frac{1}{|{U}|} \sum_{i=1}^{|{U}|} \frac{1}{rank_i}\]

\(U\) is the number of users, \(rank_i\) is the rank of the first item in the recommendation list in the test set results for user \(i\).

To compute the metric, add it to the config file adopting the following pattern:

simple_metrics: [MRR]

eval_user_metric()

Evaluation function :return: the overall averaged value of Mean Reciprocal Rank per user

static name()

Metric Name Getter :return: returns the public name of the metric

Module contents