Details

Autor(en) / Beteiligte

• Li, Shan
• Shi, Baoxu
• Yang, Jaewon
• Yan, Ji
• Wang, Shuai
• Chen, Fei
• He, Qi

Titel

Deep Job Understanding at LinkedIn

Ist Teil von

• Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, p.2145-2148

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2020

Link zum Volltext

Quelle

ACM Digital Library【キャンパス外アクセス可】

Beschreibungen/Notizen

• 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.