论文标题
显着性和市场感知的技能提取工作目标
Salience and Market-aware Skill Extraction for Job Targeting
论文作者
论文摘要
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提供了一个反应性的求职系统，以及您可能对（Jymbii）系统感兴趣的主动工作，以将最佳候选人与他们梦dream以求的工作相匹配。开发这些系统的最具挑战性的任务之一是从职位发布中正确提取重要的技能实体，然后以匹配属性为目标成员。在这项工作中，我们表明，常用的基于文本的\ emph {显着性和市场对市场的技能提取方法是最佳选择的，因为它仅考虑技能提及，而忽略了技能及其市场动态的显着水平，即市场供应和需求对技能的重要性。为了解决上述缺点，我们提出\模型，我们已部署的\ emph {显着性和市场感知}技能提取系统。提出的\模型〜在改善工作推荐的在线表现（$++1.92 \％$ abip abip）和工作海报的技能建议（$ -37 \％$ $建议拒绝率）方面显示了有希望的结果。最后，我们介绍了案例研究，以展示有趣的见解，这些见解与职业，工业，国家和个人技能水平相比，将传统技能识别方法与拟议的\模型〜进行对比。基于上述有希望的结果，我们在线部署了\ model〜在线提取工作定位技能的所有20美元$ M的职位发布。
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.
