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JobViz: Skill-driven visual exploration of job advertisements

Authors :
Ran Wang
Qianhe Chen
Yong Wang
Lewei Xiong
Boyang Shen
Source :
Visual Informatics, Vol 8, Iss 3, Pp 18-28 (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design; a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’ swift comprehension of the pertinent skills necessitated by respective positions; a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach.

Details

Language :
English
ISSN :
2468502X
Volume :
8
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Visual Informatics
Publication Type :
Academic Journal
Accession number :
edsdoj.69eb3771fd4344c39884cc130abca7c4
Document Type :
article
Full Text :
https://doi.org/10.1016/j.visinf.2024.07.001