Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations

TL;DR

This paper addresses fairness in two-sided online platforms by proposing an incremental update mechanism for recommendation algorithms, ensuring smooth producer exposure changes while maintaining customer utility.

Contribution

It introduces an ILP-based online optimization method for incremental platform updates, balancing fairness and utility in two-sided markets.

Findings

The proposed method ensures fair exposure adjustments for producers.

It maintains minimum customer utility during updates.

Evaluations show efficiency and fairness over real datasets.

Abstract

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.