JobViz:以技能为导向的招聘广告可视化探索

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Visual Informatics Pub Date : 2024-09-01 DOI:10.1016/j.visinf.2024.07.001
Ran Wang , Qianhe Chen , Yong Wang , Lewei Xiong , Boyang Shen
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引用次数: 0

摘要

各种招聘门户网站或网站上的在线招聘广告已成为时下人们寻找潜在职业机会的最流行方式。然而,这些招聘网站大多仅限于提供基本的筛选条件,如职位名称、关键字和薪酬范围。这往往会给求职者带来挑战,使他们难以在浩如烟海的招聘广告中有效识别与其独特技能相符的相关招聘广告。因此,我们提出了协调良好的可视化方法,为求职者提供三个层次的详细职位信息:技能-职位概览采用分层可视化设计,将技能组合、招聘职位以及它们之间的关系可视化;职位探索视图利用增强的雷达图字形来表示招聘职位,进一步帮助用户快速理解各个职位所需的相关技能;职位详情视图列出了所选招聘职位的具体内容,以便进行深入分析和比较。通过使用从中国最大的招聘网站之一 51Job 收集的真实招聘广告数据集,我们进行了两项案例研究和用户访谈,以评估 JobViz。结果证明了我们的方法的实用性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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. 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.

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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
期刊最新文献
Intelligent CAD 2.0 Editorial Board RelicCARD: Enhancing cultural relics exploration through semantics-based augmented reality tangible interaction design JobViz: Skill-driven visual exploration of job advertisements Visual evaluation of graph representation learning based on the presentation of community structures
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