23/08/2020

Salience and market-aware skill extraction for job targeting

Baoxu Shi, Jaewon Yang, Feng Guo, Qi He

Keywords: job targeting, skill recommendation, skill inference

Abstract Paper Similar Papers

Abstract: At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based salience and market-agnostic skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present Job2Skills, our deployed salience and market-aware skill extraction system. The proposed Job2Skills shows promising results in improving the online performance of job recommendation (JYMBII) (+1.92

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