Redesigning VolunteerMatch’s Search Algorithm: Toward More Equitable Access to Volunteers

In collaboration with VolunteerMatch (VM)—the world’s largest online platform for connecting volunteers with volunteering opportunities—we designed and implemented a new display ranking algorithm. VM’s original ranking algorithm was intended to maximize efficiency (i.e., the total number of connections), but as a consequence, it repeatedly displayed the same few opportunities at the top of its ranking, effectively limiting access to volunteers for the other opportunities. To incorporate VM’s desire for equity (defined as the weekly number of opportunities with at least one connection) along with efficiency, we propose a modeling framework for online display ranking in settings where it is important to manage the trade-off between the total number of connections and the equitable allocation of these connections. We take an adversarial approach in evaluating the performance of online algorithms and show that a class of algorithms that applies a penalty to opportunities after each connection provides a strong (and, in certain regimes, optimal) performance guarantee. Inspired by our theoretical results yet mindful of practical considerations on VM’s platform, we propose SmartSort, a simple score-based ranking algorithm that enjoys comparable guarantees in many regimes. We implemented SmartSort in two experiments, covering Dallas–Fort Worth and all of Southern California. Using a difference-in-differences analysis, we find that the implementation of SmartSort led to an estimated 8% increase in the weekly average number of opportunities with at least one connection (consistent across both experiments) without any significant decrease in the total number of connections. If SmartSort has a similar distributional effect on a national scale, an additional 30,000 connections every year will go to opportunities that would have otherwise lacked access to volunteers. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03838 .