Community-Based Data Integration of Course and Job Data in Support of Personalized Career-Education Recommendations
This addresses the challenge of skill mismatches between education and employment for students and job seekers, offering a practical tool for career guidance, though it is incremental in applying existing graph and community detection methods to a new domain.
The study tackled the problem of aligning educational courses with job market needs by integrating course descriptions and job advertisements using heterogeneous data integration and community detection, resulting in a system that provides personalized job recommendations and identifies skill gaps for re- and upskilling.
How does your education impact your professional career? Ideally, the courses you take help you identify, get hired for, and perform the job you always wanted. However, not all courses provide skills that transfer to existing and future jobs; skill terms used in course descriptions might be different from those listed in job advertisements; and there might exist a considerable skill gap between what is taught in courses and what is needed for a job. In this study, we propose a novel method to integrate extensive course description and job advertisement data by leveraging heterogeneous data integration and community detection. The innovative heterogeneous graph approach along with identified skill communities enables cross-domain information recommendation, e.g., given an educational profile, job recommendations can be provided together with suggestions on education opportunities for re- and upskilling in support of lifelong learning.