Developing a Framework for Potential Candidate Selection

Abstract

Recruitment is the process of hiring the right person for the right job. In the current competitive world, recruiting the right person from thousands of applicants is a tedious work. In addition, analyzing these huge numbers of applications manually might result into biased and erroneous output which may eventually cause problems for the companies. If these pools of resumes can be analyzed automatically and presented to the employers in a systematic way for choosing the appropriate person for their company, it may help the applicants and the employers as well. So in order to solve this need, we have developed a framework that takes the resume of the candidates, pull out information from them by recognizing the named entities using machine learning and score the applicants according to some predefined rules and employer requirements. Furthermore, employers can select the best suited candidates for their jobs from these scores by using skyline filtering.

A Bibliometric Perspective on AI Research for Job-Résumé Matching

“…Another study by [ 41 ] also proposes a framework for the ranking of candidates that initially extracts relevant information from résumés via tokenisation and NER using the spaCy library. Applicant scores are obtained according to a set of predefined rules about the extracted attributes along with their values and the employer's requirements.…”

“…RésuMatcher: a personalised résumé-job matching system [15] Highly cited (Table 2), influential paper (Figure 5), cluster centrality (Figure 10(a)) JRC : a job post and resume classification system for online recruitment, analysis, and shortcomings of e-recruitment systems [39,40] Recent timeline, authoring dynamics (Figure 3), highly cited in collection (Figure 4(b)) Developing a framework for potential candidate selection, potential candidate selection using information ex-date selection using information extraction and skyline queries [41,42] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6)…”

“…A domain-specific ESA method for semantic text matching [43] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6) A vectorisation model for job matching application of a government employment service office [44] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6) Competence-level prediction and resume and job description matching using context-aware transformer models [45] Recent timeline, authoring dynamics (Figure 3), ML or NLP keywords (Figure 6) description of this approach is provided in [39], emphasising the formalities of the semantic network construction procedure for the job and résumé models, along with similarity metrics for the matching. Another study by [41] also proposes a framework for the ranking of candidates that initially extracts relevant information from résumés via tokenisation and NER using the spaCy library. Applicant scores are obtained according to a set of predefined rules about the extracted attributes along with their values and the employer's requirements.…”