Ran Wang , Qianhe Chen , Yong Wang , Lewei Xiong , Boyang Shen
{"title":"JobViz:以技能为导向的招聘广告可视化探索","authors":"Ran Wang , Qianhe Chen , Yong Wang , Lewei Xiong , Boyang Shen","doi":"10.1016/j.visinf.2024.07.001","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>JobViz</em>. The results demonstrated the usefulness and effectiveness of our approach.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"8 3","pages":"Pages 18-28"},"PeriodicalIF":3.8000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X24000391/pdfft?md5=62d1e06a4ba3529c504c7ac24e65e000&pid=1-s2.0-S2468502X24000391-main.pdf","citationCount":"0","resultStr":"{\"title\":\"JobViz: Skill-driven visual exploration of job advertisements\",\"authors\":\"Ran Wang , Qianhe Chen , Yong Wang , Lewei Xiong , Boyang Shen\",\"doi\":\"10.1016/j.visinf.2024.07.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <em>JobViz</em>. The results demonstrated the usefulness and effectiveness of our approach.</p></div>\",\"PeriodicalId\":36903,\"journal\":{\"name\":\"Visual Informatics\",\"volume\":\"8 3\",\"pages\":\"Pages 18-28\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2468502X24000391/pdfft?md5=62d1e06a4ba3529c504c7ac24e65e000&pid=1-s2.0-S2468502X24000391-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Visual Informatics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468502X24000391\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Informatics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468502X24000391","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.