Please use this identifier to cite or link to this item:
Title: Matching applicants with positions for better allocation of employees in the job market
Authors: Elgammal Z.
Barmu A.
Hassan H.
Elgammal K.
Özyer, Tansel
Alhajj R.
Keywords: Job recommendations
Matching systems
Natural language processing
Screening systems
Hunting process
Job applicant
Job hunting
Job market
Job recommendation
Matching system
Screening system
Speed up
Natural language processing systems
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Elgammal, Z., Barmu, A., Hassan, H., Elgammal, K., Özyer, T., & Alhajj, R. (2021, December). Matching applicants with positions for better allocation of employees in the job market. In 2021 22nd International Arab Conference on Information Technology (ACIT) (pp. 1-5). IEEE.
Abstract: Nowadays most people who are looking for a job use the Internet, visiting websites like Linkedin or Indeed so they must face hundreds of recruitment companies and job ads. The process of applying for a job is time consuming especially in screening, preparing and attending tests and interviews. In addition, job applicants do not know which companies are most proper for them, this job-hunting strategy can easily lead to employment dissatisfaction or failure. therefore, it is more efficient to recommend a few most suitable jobs. Also, manual screening for a position is time consuming and expensive. Experienced recruiters may be able to speed up the process by noting patterns in the resumes. The aim is therefore also to identify these patterns so they can be implemented in the system. In this work we try to introduce a system that would help both recruiters and job applicants in the job-hunting process. © 2021 IEEE.
Description: 22nd International Arab Conference on Information Technology, ACIT 2021 -- 21 December 2021 through 23 December 2021 -- -- 176492
ISBN: 9781665419956
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.