  When devising recommendation services, it is important to account for the
interests of all content providers, encompassing not only newcomers but also
minority demographic groups. In various instances, certain provider groups find
themselves underrepresented in the item catalog, a situation that can influence
recommendation results. Hence, platform owners often seek to regulate the
exposure of these provider groups in the recommended lists. In this paper, we
propose a novel cost-sensitive approach designed to guarantee these target
exposure levels in pairwise recommendation models. This approach quantifies,
and consequently mitigate, the discrepancies between the volume of
recommendations allocated to groups and their contribution in the item catalog,
under the principle of equity. Our results show that this approach, while
aligning groups exposure with their assigned levels, does not compromise to the
original recommendation utility. Source code and pre-processed data can be
retrieved at
https://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure.
