Career Aura – Smart Resume and Employment Recommender

Kavindi Dissanayake, Senidu Mendis, Randula Subasinghe, Dineth Geethanjana, Dilani Lunugalage, D. Kasthurirathna
{"title":"Career Aura – Smart Resume and Employment Recommender","authors":"Kavindi Dissanayake, Senidu Mendis, Randula Subasinghe, Dineth Geethanjana, Dilani Lunugalage, D. Kasthurirathna","doi":"10.1109/ICAC54203.2021.9671212","DOIUrl":null,"url":null,"abstract":"Recruitment and Job seeking are two major factors that are directly proportional to each other. Due to the competitive nature of the present world, the process of acquiring the best resource effectively and efficiently has become a challenging aspect for the companies. As a result, modern job portals have become increasingly popular to address the challenges identified in the early recruitment and job search process. The purpose of this research is to introduce an optimal solution to address the ineffective areas identified in the job and recruitment domain which can further enhance the recruitment and job seeking decisions by utilizing deep learning and sentiment analytic approach along with descriptive analysis. The proposed system recommends the relevant job opportunities by omitting the irrelevant job advertisements for job hunters who are interested in the IT job domain while they input their resume to the system and additionally, they can improve their career decisions by adhering to the prediction schemes. Moreover, the system facilitates recruiters to headhunt top talents efficiently once they input job requirements to the system and candidate suggestions are not only made depending on their resume information but also analyzing their LinkedIn endorsements.","PeriodicalId":227059,"journal":{"name":"2021 3rd International Conference on Advancements in Computing (ICAC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Advancements in Computing (ICAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAC54203.2021.9671212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Recruitment and Job seeking are two major factors that are directly proportional to each other. Due to the competitive nature of the present world, the process of acquiring the best resource effectively and efficiently has become a challenging aspect for the companies. As a result, modern job portals have become increasingly popular to address the challenges identified in the early recruitment and job search process. The purpose of this research is to introduce an optimal solution to address the ineffective areas identified in the job and recruitment domain which can further enhance the recruitment and job seeking decisions by utilizing deep learning and sentiment analytic approach along with descriptive analysis. The proposed system recommends the relevant job opportunities by omitting the irrelevant job advertisements for job hunters who are interested in the IT job domain while they input their resume to the system and additionally, they can improve their career decisions by adhering to the prediction schemes. Moreover, the system facilitates recruiters to headhunt top talents efficiently once they input job requirements to the system and candidate suggestions are not only made depending on their resume information but also analyzing their LinkedIn endorsements.
职业光环-聪明的简历和就业推荐
招聘和求职是两个主要因素,它们成正比。由于当今世界的竞争性质,有效和高效地获取最佳资源的过程已成为公司面临的一个挑战。因此,现代求职门户网站越来越受欢迎,以解决早期招聘和求职过程中发现的挑战。本研究的目的是通过利用深度学习和情感分析方法以及描述性分析,引入一种最优解决方案来解决工作和招聘领域中发现的无效区域,从而进一步提高招聘和求职决策。该系统为对IT工作领域感兴趣的求职者在向系统输入简历的过程中,通过删除不相关的招聘广告,推荐相关的工作机会,并通过遵守预测方案来改善职业选择。此外,一旦招聘人员将工作要求输入系统,招聘人员就可以有效地寻找到顶尖人才,候选人的建议不仅取决于他们的简历信息,还会根据他们在LinkedIn上的背书进行分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信