SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights

TL;DR

SkillRec is a data-driven system that leverages embedding techniques and neural networks to recommend relevant job skills based on job titles, aiding career insights amidst rapidly evolving industry requirements.

Contribution

It introduces a novel neural network-based approach for skill recommendation using job descriptions and embedding techniques, addressing the challenge of dynamic skill requirements.

Findings

Achieved promising accuracy and F1-score on a dataset of 6,000 job titles.

Demonstrated effectiveness of embedding and neural network techniques for skill prediction.

Provides a scalable method for identifying relevant skills for various job roles.

Abstract

Understanding the skill sets and knowledge required for any career is of utmost importance, but it is increasingly challenging in today's dynamic world with rapid changes in terms of the tools and techniques used. Thus, it is especially important to be able to accurately identify the required skill sets for any job for better career insights and development. In this paper, we propose and develop the Skill Recommendation (SkillRec) system for recommending the relevant job skills required for a given job based on the job title. SkillRec collects and identify the skill set required for a job based on the job descriptions published by companies hiring for these roles. In addition to the data collection and pre-processing capabilities, SkillRec also utilises word/sentence embedding techniques for job title representation, alongside a feed-forward neural network for job skill recommendation based on the job title representation. Based on our preliminary experiments on a dataset of 6,000 job titles and descriptions, SkillRec shows a promising performance in terms of accuracy and F1-score.