Community-Based Data Integration of Course and Job Data in Support of Personalized Career-Education Recommendations

June 24, 2020 Β· Declared Dead Β· πŸ› ASIS&T Annual Meeting

πŸ‘» CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Guoqing Zhu, Naga Anjaneyulu Kopalle, Yongzhen Wang, Xiaozhong Liu, Kemi Jona, Katy BΓΆrner arXiv ID 2006.13864 Category cs.IR: Information Retrieval Citations 11 Venue ASIS&T Annual Meeting Last Checked 4 months ago
Abstract
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.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Information Retrieval

Died the same way β€” πŸ‘» Ghosted