Deep Job Understanding at LinkedIn
This work addresses job matching efficiency for LinkedIn's users, but it appears incremental as it applies deep transfer learning to a specific domain without introducing a new paradigm.
The paper tackled the problem of understanding unstructured and noisy job postings to improve job targeting accuracy and hire efficiency at LinkedIn, resulting in significant metric lifts in the job recommendation system and increased job poster satisfaction.
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.