JobViz: skill-driven visual exploration of job advertisements
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. Thi...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181447 |
| _version_ | 1826109662751096832 |
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| author | Wang, Ran Chen, Qianhe Wang, Yong Xiong, Lewei Shen, Boyang |
| author2 | College of Computing and Data Science |
| author_facet | College of Computing and Data Science Wang, Ran Chen, Qianhe Wang, Yong Xiong, Lewei Shen, Boyang |
| author_sort | Wang, Ran |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-03-09T09:51:24Z |
| format | Journal Article |
| id | ntu-10356/181447 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T09:51:24Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1814472024-12-02T06:45:59Z JobViz: skill-driven visual exploration of job advertisements Wang, Ran Chen, Qianhe Wang, Yong Xiong, Lewei Shen, Boyang College of Computing and Data Science Computer and Information Science Visual exploration Job advertisements 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. Published version This research is founded by Huazhong University of Science and Technology Teaching Research Project number(s): 2023100. 2024-12-02T06:45:59Z 2024-12-02T06:45:59Z 2024 Journal Article Wang, R., Chen, Q., Wang, Y., Xiong, L. & Shen, B. (2024). JobViz: skill-driven visual exploration of job advertisements. Visual Informatics, 8(3), 18-28. https://dx.doi.org/10.1016/j.visinf.2024.07.001 2468-502X https://hdl.handle.net/10356/181447 10.1016/j.visinf.2024.07.001 2-s2.0-85203497756 3 8 18 28 en Visual Informatics © 2024 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
| spellingShingle | Computer and Information Science Visual exploration Job advertisements Wang, Ran Chen, Qianhe Wang, Yong Xiong, Lewei Shen, Boyang JobViz: skill-driven visual exploration of job advertisements |
| title | JobViz: skill-driven visual exploration of job advertisements |
| title_full | JobViz: skill-driven visual exploration of job advertisements |
| title_fullStr | JobViz: skill-driven visual exploration of job advertisements |
| title_full_unstemmed | JobViz: skill-driven visual exploration of job advertisements |
| title_short | JobViz: skill-driven visual exploration of job advertisements |
| title_sort | jobviz skill driven visual exploration of job advertisements |
| topic | Computer and Information Science Visual exploration Job advertisements |
| url | https://hdl.handle.net/10356/181447 |
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