  Fairness in recommender systems has been considered with respect to sensitive
attributes of users (e.g., gender, race) or items (e.g., revenue in a
multistakeholder setting). Regardless, the concept has been commonly
interpreted as some form of equality -- i.e., the degree to which the system is
meeting the information needs of all its users in an equal sense. In this
paper, we argue that fairness in recommender systems does not necessarily imply
equality, but instead it should consider a distribution of resources based on
merits and needs.
  We present a probabilistic framework based on generalized cross entropy to
evaluate fairness of recommender systems under this perspective, where we show
that the proposed framework is flexible and explanatory by allowing to
incorporate domain knowledge (through an ideal fair distribution) that can help
to understand which item or user aspects a recommendation algorithm is over- or
under-representing. Results on two real-world datasets show the merits of the
proposed evaluation framework both in terms of user and item fairness.
