"""
This is the implementation of the User Coverage metric.
It directly proceeds from a system-wise computation, and it considers all the users at the same time.
"""
__version__ = '0.3.1'
__author__ = 'Vito Walter Anelli, Claudio Pomo'
__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'
from elliot.evaluation.metrics.base_metric import BaseMetric
[docs]class UserCoverage(BaseMetric):
r"""
User Coverage
This class represents the implementation of the User Coverage recommendation metric.
For further details, please refer to the `book <https://link.springer.com/10.1007/978-1-4939-7131-2_110158>`_
Note:
The proportion of users or user interactions for which the system can recommend items. In many applications
the recommender may not provide recommendations for some users due to, e.g. low confidence in the accuracy
of predictions for that user.
To compute the metric, add it to the config file adopting the following pattern:
.. code:: yaml
simple_metrics: [UserCoverage]
"""
def __init__(self, recommendations, config, params, eval_objects):
"""
Constructor
:param recommendations: list of recommendations in the form {user: [(item1,value1),...]}
:param config: SimpleNameSpace that represents the configuration of the experiment
:param params: Parameters of the model
:param eval_objects: list of objects that may be useful for the computation of the different metrics
"""
super().__init__(recommendations, config, params, eval_objects)
[docs] @staticmethod
def name():
"""
Metric Name Getter
:return: returns the public name of the metric
"""
return "UserCoverage"
[docs] def eval(self):
"""
Evaluation function
:return: the overall averaged value of User Coverage
"""
return sum([1 if len(u_r) > 0 else 0 for u_r in self._recommendations.values()])