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- W3030917635 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 emph{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 model, our deployed emph{salience and market-aware} skill extraction system. The proposed model ~shows promising results in improving the online performance of job recommendation (JYMBII) ($+1.92%$ job apply) and skill suggestions for job posters ($-37%$ suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed model~from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the model ~online to extract job targeting skills for all $20$M job postings served at LinkedIn." @default.
- W3030917635 created "2020-06-05" @default.
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- W3030917635 date "2020-05-26" @default.
- W3030917635 modified "2023-09-26" @default.
- W3030917635 title "Salience and Market-aware Skill Extraction for Job Targeting" @default.
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- W3030917635 doi "https://doi.org/10.48550/arxiv.2005.13094" @default.
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