学习询问招聘信息的筛选问题
Learning to Ask Screening Questions for Job Postings
摘要：
在LinkedIn，我们希望为全球劳动力中的每个人创造经济机会。这一目标的一个关键方面是将工作与合格的申请人相匹配。为了提高招聘效率并减少人工筛选每个申请人的需要，我们开发了一种新产品，招聘人员可以在线询问筛选问题，以便他们可以轻松筛选合格的候选人。为了向LinkedIn上的所有$20$ M活动职位添加筛选问题，我们提出了一个新任务，旨在为给定的职位发布自动生成筛选问题。为了解决生成筛选问题的任务，我们开发了一个称为Job 2 Questions的两阶段深度学习模型，在该模型中，我们应用深度学习模型从文本描述中检测意图，然后根据其他上下文特征按其重要性对检测到的意图进行排名。由于这是一个没有历史数据的新产品，我们采用深度迁移学习来训练具有有限训练数据的复杂模型。我们向LinkedIn用户推出了筛选问题产品和人工智能模型，并观察到了对就业市场的重大影响。在我们的在线A/B测试中，我们观察了$+53.10\%$筛选问题建议接受率、$+22.17\%$职位覆盖率、$+190\%$招聘者-申请者互动以及$+11$净推荐分数。总之，部署的Job 2 Questions模型可以帮助招聘人员找到合格的申请人，帮助求职者找到他们适合的工作。
英文摘要：
At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiring efficiency and reduce the need to manually screening each applicant, we develop a new product where recruiters can ask screening questions online so that they can filter qualified candidates easily. To add screening questions to all $20$M active jobs at LinkedIn, we propose a new task that aims to automatically generate screening questions for a given job posting. To solve the task of generating screening questions, we develop a two-stage deep learning model called Job2Questions, where we apply a deep learning model to detect intent from the text description, and then rank the detected intents by their importance based on other contextual features. Since this is a new product with no historical data, we employ deep transfer learning to train complex models with limited training data. We launched the screening question product and our AI models to LinkedIn users and observed significant impact in the job marketplace. During our online A/B test, we observed $+53.10\%$ screening question suggestion acceptance rate, $+22.17\%$ job coverage, $+190\%$ recruiter-applicant interaction, and $+11$ Net Promoter Score. In sum, the deployed Job2Questions model helps recruiters to find qualified applicants and job seekers to find jobs they are qualified for.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
