  Engaging all content providers, including newcomers or minority demographic
groups, is crucial for online platforms to keep growing and working. Hence,
while building recommendation services, the interests of those providers should
be valued. In this paper, we consider providers as grouped based on a common
characteristic in settings in which certain provider groups have low
representation of items in the catalog and, thus, in the user interactions.
Then, we envision a scenario wherein platform owners seek to control the degree
of exposure to such groups in the recommendation process. To support this
scenario, we rely on disparate exposure measures that characterize the gap
between the share of recommendations given to groups and the target level of
exposure pursued by the platform owners. We then propose a re-ranking procedure
that ensures desired levels of exposure are met. Experiments show that, while
supporting certain groups of providers by rendering them with the target
exposure, beyond-accuracy objectives experience significant gains with
negligible impact in recommendation utility.
