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.

该研究论文介绍了 LinkedIn 如何利用深度迁移学习方法和专家反馈循环来改进其职位理解模型，从而提高职位推荐系统的准确性和雇佣效率。