JPLink: On Linking Jobs to Vocational Interest Types
This addresses the challenge of linking job seekers to suitable jobs based on personality types for career services, but it is incremental as it builds on existing RIASEC frameworks and ranking methods.
The paper tackled the problem of automatically assigning RIASEC vocational interest labels to job posts, which is labor-intensive to do manually, and proposed JPLink, a machine learning approach that outperforms conventional baselines in evaluations.
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.