Details

Autor(en) / Beteiligte

• Shi, Baoxu
• Li, Shan
• Yang, Jaewon
• Kazdagli, Mustafa Emre
• He, Qi

Titel

Learning to Ask Screening Questions for Job Postings

Ist Teil von

• Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, p.549-558

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2020

Link zum Volltext

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiring efficiency and reduce the need to manually screening each applicant, we develop a new product where recruiters can ask screening questions online so that they can filter qualified candidates easily. To add screening questions to all 20M active jobs at Linked In, we propose a new task that aims to automatically generate screening questions for a given job posting. To solve the task of generating screening questions, we develop a two-stage deep learning model called Job2Questions, where we apply a deep learning model to detect intent from the text description, and then rank the detected intents by their importance based on other contextual features. Since this is a new product with no historical data, we employ deep transfer learning to train complex models with limited training data. We launched the screening question product and our AI models to LinkedIn users and observed significant impact in the job marketplace. During our online A/B test, we observed +53.10% screening question suggestion acceptance rate, +22.17% job coverage, +190% recruiter-applicant interaction, and +11 Net Promoter Score. In sum, the deployed Job2Questions model helps recruiters to find qualified applicants and job seekers to find jobs they are qualified for.