Fuzzy-based intelligent shortlisting process for human resource job recruitment procedures

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.