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Download fileTowards automating the recruitment process
thesis
posted on 2022-03-28, 16:07 authored by Usman ShahbazBusiness world is getting increasingly dynamic. Information processing using knowledge-, service-, and cloud-based systems makes the use of complex, dynamic and often knowledge-intensive activities an inevitable task. Knowledge-intensive processes contain a set of coordinated tasks and activities, controlled by knowledge workers to achieve a business objective or goal. Talent acquisition and recruitment processes - i.e., the process of identifying the jobs vacancy, analyzing the job requirements, reviewing applications, screening, shortlisting and selecting the right candidate - are example of Knowledge-intensive processes. Attracting and recruiting right talent is a key differentiator in modern organizations.
In this thesis, we analyze the state of the art in traditional recruitment model and identify the main gaps when evaluating candidate profile with position description. We put the first step towards automating the recruitment process. We present a framework and algorithms to: imitate the knowledge of recruiters into the domain knowledge, extract data and knowledge from business artifacts, e.g., candidates' CV and position description, and link them to the facts in the domain Knowledge Base. We develop a digital dashboard to help recruiters draw insights in a quick and easy way. We adopt a motivating scenario in recruiting a Data Scientist role in an organization, and conduct a user study to highlight how iRecruit significantly facilitates the knowledge intensive tasks in the recruitment process.
History
Table of Contents
1. Introduction -- 2. Background and state-of-the-art -- 3. Towards automating recruitment process -- 4. Experiment and evaluation -- 5. Conclusion and future directions -- Appendix -- References.Notes
Theoretical thesis. Bibliography: pages 44-58Awarding Institution
Macquarie UniversityDegree Type
Thesis MResDegree
MRes, Macquarie University, Faculty of Science and Engineering, Department of ComputingDepartment, Centre or School
Department of ComputingYear of Award
2020Principal Supervisor
Amin BeheshtiRights
Copyright Usman Shahbaz 2020. Copyright disclaimer: http://mq.edu.au/library/copyrightLanguage
EnglishExtent
1 online resource (xvi, 58 pages)Former Identifiers
mq:71676 http://hdl.handle.net/1959.14/1276947Usage metrics
Categories
Keywords
HR analyticsRecruitment knowledge baseInformation storage and retrieval systemsProcess data analyticsProcess data scienceData-driven business processesBig data analyticsElectronic data processingPosition description match scoreKnowledge-intensive business processesRecruitment processEmployees RecruitingArtificial intelligence