Salience and Market-aware Skill Extraction for Job Targeting

TL;DR

This paper introduces a novel salience and market-aware skill extraction system for LinkedIn that improves job matching and skill suggestion accuracy by considering skill importance and market dynamics, outperforming traditional methods.

Contribution

The paper presents extsc{SkillX}, a deployed system that incorporates salience and market awareness into skill extraction, enhancing job recommendation and skill suggestion effectiveness.

Findings

Improved job application rate by 1.92% using extsc{SkillX}

Reduced skill suggestion rejection rate by 37%

Deployed online for 20 million job postings at LinkedIn

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.