Fair Updates in Two-Sided Market Platforms: On Incrementally Updating Recommendations

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 η 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. Introduction Many popular online platforms today can be thought of as two-sided markets, such as, sharing economy platforms like Uber, Lyft or Airbnb, e-commerce sites like Amazon, news aggregation services like Google News, location-based review and recommendation services like Yelp, Google Local, employment sites like LinkedIn, Indeed, or hotel aggregators like Booking.com. There are three stakeholders in these markets: (i) producers of goods and services (e.g., sellers in Amazon, hosts in Airbnb), (ii) customers who pay for them, and (iii) the platform at the center of the ecosystem. Services on these platforms have traditionally been designed to maximize customer satisfaction, since they are the ones directly contributing to the platform revenue, largely ignoring the interest of the other key stakeholder -producers.