论文标题
使用生成对抗网络在招聘市场中提供可行的反馈
Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks
论文作者
论文摘要
机器学习预测因素已越来越多地用于生产环境中，包括在世界上最大的招聘平台之一中，以提供更好的候选人和招聘经验。希望提供可行的反馈的能力对于候选人提高了在市场上取得成功的机会。但是，直到最近，旨在提供可行反馈的方法在现实主义和延迟方面一直受到限制。在这项工作中，我们演示了如何通过基于生成对抗网络（GAN）应用新介绍的方法，我们能够克服这些限制并实时向生产设置中的候选人实时提供可行的反馈。我们的实验结果突出了相对于其他两种最先进的方法（包括超过1000倍的延迟增长），在数据集中使用基于GAN的方法的重大好处。我们还详细说明了这种方法对两个真正的候选概况示例的潜在影响。
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.
