Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2020

Abstract

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits domain knowledge available in an occupation-specific knowledge base known as O*NET to improve feature representation of job posts. To incorporate relative ranking of RIASEC labels of each job, JPLink proposes a listwise loss function inspired by learning to rank. Both our quantitative and qualitative evaluations show that JPLink outperforms conventional baselines. We conduct an error analysis on JPLink’s predictions to show that it can uncover label errors in existing job posts.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

PAKDD2020: The 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 11-14 May 2020

First Page

220

Last Page

232

Identifier

10.1007/978-3-030-47436-2_17

City or Country

Online

Additional URL

https://doi.org/10.1007/978-3-030-47436-2_17

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