Log In Sign Up

JPLink: On Linking Jobs to Vocational Interest Types

by   Amila Silva, et al.

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.


page 1

page 2

page 3

page 4


Discovering Job Preemptions in the Open Science Grid

The Open Science Grid(OSG) is a world-wide computing system which facili...

What the Language You Tweet Says About Your Occupation

Many aspects of people's lives are proven to be deeply connected to thei...

Low-latency job scheduling with preemption for the development of deep learning

One significant challenge in the job scheduling of computing clusters fo...

Learning Transferrable Representations of Career Trajectories for Economic Prediction

Understanding career trajectories – the sequences of jobs that individua...

Learning Effective Representations for Person-Job Fit by Feature Fusion

Person-job fit is to match candidates and job posts on online recruitmen...

An Update to the Minho Quotation Resource

The Minho Quotation Resource was originally released in 2012. It provide...