Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks
This addresses the need for candidates to improve their success in hiring marketplaces, though it appears incremental as it adapts an existing GAN method to a specific domain.
The paper tackled the problem of providing realistic and low-latency actionable feedback to candidates on a hiring platform by applying a GAN-based method, resulting in over 1000x latency gains compared to other state-of-the-art approaches.
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.