  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.
