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iRecruit: Towards Automating the Recruitment Process

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Service Research and Innovation (ASSRI 2018, ASSRI 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 367))

Abstract

Business 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. Recruitment process - i.e., the process of attracting, shortlisting, selecting and appointing suitable candidates for jobs within an organization - is an example of a knowledge-intensive process, where recruiters (i.e., knowledge workers who have the experience, understanding, information, and skills) control various tasks from advertising positions to analyzing the candidates’ Curriculum Vitae. Attracting and recruiting right talent is a key differentiator in modern organizations. In this paper, we put the first step towards automating the recruitment process. We present a framework and algorithms (namely iRecruit) to: (i) imitate the knowledge of recruiters into the domain knowledge; and (ii) extract data and knowledge from business artifacts (e.g., candidates’ CV and job advertisements) and link them to the facts in the domain Knowledge Base. We adopt a motivating scenario of recruitment challenges to find the right fit for Data Scientists role in an organization.

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Notes

  1. 1.

    A Curriculum Vitae (CV) is a written overview of a person’s experience and other qualifications for a job opportunity. It is akin to a résumé in North America.

  2. 2.

    A Knowledge Base (KB) consists of a set of concepts organized into a taxonomy, instances for each concept and relationships among the concepts [6].

  3. 3.

    https://builtvisible.com/recruitment-seo-guide/.

  4. 4.

    https://www.timeshighereducation.com.

  5. 5.

    https://www.topuniversities.com/qs-world-university-rankings.

  6. 6.

    https://www.glassdoor.com.au/Reviews/index.htm.

  7. 7.

    https://greatplacetowork.com.au/.

  8. 8.

    https://www.acs.org.au.

  9. 9.

    https://www.webopedia.com/quick_ref/computer-certifications.html.

  10. 10.

    https://en.wikipedia.org/wiki/Academic/_certificate.

  11. 11.

    https://wordnet.princeton.edu/.

  12. 12.

    https://developers.google.com/knowledge-graph/.

  13. 13.

    https://www.wikidata.org/.

  14. 14.

    https://github.com/unsw-cse-soc/CoreKG.

  15. 15.

    https://developers.google.com/knowledge-graph/.

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Author information

Authors and Affiliations

  1. Macquarie University, Sydney, Australia

    Usman Shahbaz, Amin Beheshti & Mehregan Mahdavi

  2. Rakuten, Inc., Tokyo, Japan

    Sadegh Nobari

  3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Qiang Qu

  4. University of New South Wales, Sydney, Australia

    Hye-Young Paik

  5. Asia Pacific International College (APIC), Sydney, Australia

    Mehregan Mahdavi

Authors
  1. Usman Shahbaz
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  2. Amin Beheshti
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  3. Sadegh Nobari
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  4. Qiang Qu
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  5. Hye-Young Paik
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  6. Mehregan Mahdavi
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Corresponding author

Correspondence to Amin Beheshti .

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Editors and Affiliations

  1. CSIRO, Eveleigh, NSW, Australia

    Ho-Pun Lam

  2. University of Sydney, Sydney, NSW, Australia

    Sajib Mistry

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Shahbaz, U., Beheshti, A., Nobari, S., Qu, Q., Paik, HY., Mahdavi, M. (2019). iRecruit: Towards Automating the Recruitment Process. In: Lam, HP., Mistry, S. (eds) Service Research and Innovation. ASSRI ASSRI 2018 2018. Lecture Notes in Business Information Processing, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-030-32242-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-32242-7_11

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