  Major online platforms today can be thought of as two-sided markets with
producers and customers of goods and services. There have been concerns that
over-emphasis on customer satisfaction by the platforms may affect the
well-being of the producers. To counter such issues, few recent works have
attempted to incorporate fairness for the producers. However, these studies
have overlooked an important issue in such platforms -- to supposedly improve
customer utility, the underlying algorithms are frequently updated, causing
abrupt changes in the exposure of producers. In this work, we focus on the
fairness issues arising out of such frequent updates, and argue for incremental
updates of the platform algorithms so that the producers have enough time to
adjust (both logistically and mentally) to the change. However, naive
incremental updates may become unfair to the customers. Thus focusing on
recommendations deployed on two-sided platforms, we formulate an ILP based
online optimization to deploy changes incrementally in n steps, where we can
ensure smooth transition of the exposure of items while guaranteeing a minimum
utility for every customer. Evaluations over multiple real world datasets show
that our proposed mechanism for platform updates can be efficient and fair to
both the producers and the customers in two-sided platforms.
