Learning to Match

TL;DR

This paper addresses the complex problem of matching multiple skilled workers to multiple tasks with uncertain qualities, proposing new algorithms that outperform standard methods and approach oracle performance.

Contribution

It introduces algorithms for multi-skill, multi-task matching under uncertainty, extending beyond single-skill bandit problems, with experimental validation.

Findings

One algorithm achieves up to 85% of oracle performance.

Experimental results compare favorably with bounded ε-first algorithms.

Highlights the need for real-world benchmarks in matching under uncertainty.

Abstract

Outsourcing tasks to previously unknown parties is becoming more common. One specific such problem involves matching a set of workers to a set of tasks. Even if the latter have precise requirements, the quality of individual workers is usually unknown. The problem is thus a version of matching under uncertainty. We believe that this type of problem is going to be increasingly important. When the problem involves only a single skill or type of job, it is essentially a type of bandit problem, and can be solved with standard algorithms. However, we develop an algorithm that can perform matching for workers with multiple skills hired for multiple jobs with multiple requirements. We perform an experimental evaluation in both single-task and multi-task problems, comparing with the bounded $\epsilon$-first algorithm, as well as an oracle that knows the true skills of workers. One of the algorithms we developed gives results approaching 85\% of oracle's performance. We invite the community to take a closer look at this problem and develop real-world benchmarks.