面向工作目标的显著性和市场感知技能提取
Salience and Market-aware Skill Extraction for Job Targeting
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。为了实现这一目标，LinkedIn提供了一个反应式的求职系统，以及一个主动式的你可能感兴趣的工作（JYMBII）系统，以匹配最好的候选人与他们的梦想工作。开发这些系统的最具挑战性的任务之一是正确地提取重要的技能实体从职位发布，然后与匹配的属性的目标成员。在这项工作中，我们表明，常用的基于文本的\emph{salience and market-agnostic}技能提取方法是次优的，因为它只考虑技能提及，忽略了技能的显着水平及其市场动态，即，市场供求关系影响技能的重要性。为了解决上述缺点，我们提出了\model，我们部署的\emph{salience and market-aware}技能提取系统。所提出的\model ~在提高在线工作推荐（JYMBII）（$+1.92\%$工作申请）和针对工作海报的技能建议（$-37\%$建议拒绝率）方面显示了有希望的结果。最后，我们提出的案例研究显示有趣的见解，对比传统的技能识别方法和建议\model ~从职业，行业，国家和个人的技能水平。基于上述有希望的结果，我们部署了\model ~online，以提取LinkedIn上所有$20$ M职位发布的工作定位技能。
英文摘要：
At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based \emph{salience and market-agnostic} skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present \model, our deployed \emph{salience and market-aware} skill extraction system. The proposed \model ~shows promising results in improving the online performance of job recommendation (JYMBII) ($+1.92\%$ job apply) and skill suggestions for job posters ($-37\%$ suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed \model~from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the \model ~online to extract job targeting skills for all $20$M job postings served at LinkedIn.
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。
