Deep Job Understanding at LinkedIn

TL;DR

This paper presents a deep transfer learning approach for understanding unstructured job postings at LinkedIn, enhancing job matching accuracy and user satisfaction through continuous feedback integration.

Contribution

It introduces a domain-specific deep learning model for job understanding and a feedback loop for ongoing model improvement within LinkedIn's platform.

Findings

Improved job recommendation metrics

Enhanced job poster satisfaction

Effective integration of deep models into production

Abstract

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.