Fuzzy-based Intelligent Shortlisting Process for Human Resource Job Recruitment Procedures

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    A fuzzy-based approach is used to simplify the process of shortlisting large number of job applications by systematically ranking individual applications primary according to their educational background, number of years of experience, and skill competencies that will match the employment position being offered. The proposed algorithm gives a full correlation of the applicant’s qualifications and to the job requirement of the company. Three important outputs are delivered by this intelligent algorithm such as the naïve qualifier, job match and the final shortlist score. The naïve qualifier gives a score that balances the educational attainment and the number of years of experience of the applicant. The job match score matches the competency or current job level of the applicant to the job level being offered. And lastly, the intelligent shortlist score which is the overall score that balances all the qualifications of an applicant such as educational attainment, years of experience and current job level. Results showed that the proposed algorithm can quantitatively analyze individual qualifications and rank the applicants effectively. The proposed algorithm will be used in the first stage of the recruitment process dealing with large number of applicants for shortlisting purposes



  • Keywords

    Fuzzy logic, intelligent algorithm, HR analytics, shortlisting, management.

  • References

      [1] Chien, Chen-Fu; Chen, Li-Fei. “Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry.” Expert Systems with Applications, Vol. 34.1, pp. 280-290, 2008.

      [2] Jantan, H., Hamdan, A. R., & Othman, Z. A. “Human talent prediction in HRM using C4. 5 classification algorithm.” International Journal on Computer Science and Engineering, Vol. 2.8, pp.2526-2534, 2010.

      [3] Kalugina, E., & Shvydun, S. “An effective personnel selection model.” Procedia Computer Science, Vol. 31, pp. 1102-1106, 2014.

      [4] Lin, H. T. “Personnel selection using analytic network process and fuzzy data envelopment analysis approaches.” Computers & Industrial Engineering, Vol. 59. 4, pp. 937-944, 2010.

      [5] Sivaram, N., & Ramar, K. “Applicability of clustering and classification algorithms for recruitment data mining.” International Journal of Computer Applications, Vol. 4.5, pp. 23-28, 2010.

      [6] Ahmadi, M., Malafe, N., & Baei, F. “Investigating the relationship between the use of ICT and human resource empowerment in Iran municipality (case study: Mazandaran province).” International Journal of Engineering & Technology, Vol. 7. 3.19, pp. 173-179, 2018.

      [7] Frank, F., Tim, W., & Tobias, K. “An automated recommendation approach to selection in personnel recruitment.” The Fifth Framework Programme Information Society Technologies, pp. 41-48, 2000

      [8] Gadi, Dung P., & Hung, D. “Human resource management practices and turnover intention: the mediating role of perceived organizational support in tertiary institutions in Nigeria.” Internation

      [9] al Journal of Engineering & Technology, Vol. 7. 3.25, pp. 715-722, 2018.




Article ID: 27834
DOI: 10.14419/ijet.v7i4.16.27834

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.