Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based method to generate real-time, actionable feedback for candidates in hiring platforms, significantly improving realism and reducing latency compared to previous approaches.

Contribution

The paper presents a novel GAN-based approach for providing real-time, actionable feedback in hiring marketplaces, overcoming previous limitations in realism and latency.

Findings

Achieved over 1000x latency improvements compared to state-of-the-art methods.

Demonstrated effectiveness on real candidate profiles.

Significant benefits shown on large-scale dataset.

Abstract

Machine learning predictors have been increasingly applied in production settings, including in one of the world's largest hiring platforms, Hired, to provide a better candidate and recruiter experience. The ability to provide actionable feedback is desirable for candidates to improve their chances of achieving success in the marketplace. Until recently, however, methods aimed at providing actionable feedback have been limited in terms of realism and latency. In this work, we demonstrate how, by applying a newly introduced method based on Generative Adversarial Networks (GANs), we are able to overcome these limitations and provide actionable feedback in real-time to candidates in production settings. Our experimental results highlight the significant benefits of utilizing a GAN-based approach on our dataset relative to two other state-of-the-art approaches (including over 1000x latency gains). We also illustrate the potential impact of this approach in detail on two real candidate profile examples.