深入了解LinkedIn的工作
Deep Job Understanding at LinkedIn
摘要：
作为世界上最大的专业网络，LinkedIn希望为全球劳动力中的每个人创造经济机会。它最重要的任务之一是将工作与游行队伍相匹配。提高工作定位的准确性和招聘效率符合LinkedIn的会员第一座右铭。为了实现这些目标，我们需要了解包含嘈杂信息的非结构化职位发布。我们应用深度迁移学习来创建特定领域的工作理解模型。在此之后，工作由专业实体表示，包括头衔，技能，公司和评估问题。为了持续提升LinkedIn的工作理解能力，我们设计了一个专家反馈回路，将工作理解模型集成到LinkedIn的产品中，以收集求职者的反馈。在这个演示中，我们介绍了LinkedIn的职位发布流程，并展示了集成的深度职位理解工作如何提高职位发布者的满意度，并在LinkedIn的职位推荐系统中提供了显着的指标提升。
英文摘要：
As the world's largest professional network, LinkedIn wants to create economic opportunity for everyone in the global workforce. One of its most critical missions is matching jobs with processionals. Improving job targeting accuracy and hire efficiency align with LinkedIn's Member First Motto. To achieve those goals, we need to understand unstructured job postings with noisy information. We applied deep transfer learning to create domain-specific job understanding models. After this, jobs are represented by professional entities, including titles, skills, companies, and assessment questions. To continuously improve LinkedIn's job understanding ability, we designed an expert feedback loop where we integrated job understanding models into LinkedIn's products to collect job posters' feedback. In this demonstration, we present LinkedIn's job posting flow and demonstrate how the integrated deep job understanding work improves job posters' satisfaction and provides significant metric lifts in LinkedIn's job recommendation system.
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。
