A Bibliometric Perspective on AI Research for Job-Résumé Matching
The search for the right person for the right job, or in other words the selection of the candidate who best reflects the skills demanded by employers to perform a specific set of duties in a job appointment, is a key premise of the personnel selection pipeline of recruitment departments. This task...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2022/8002363 |
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| _version_ | 1832548623356788736 |
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| author | Sergio Rojas-Galeano Jorge Posada Esteban Ordoñez |
| author_facet | Sergio Rojas-Galeano Jorge Posada Esteban Ordoñez |
| author_sort | Sergio Rojas-Galeano |
| collection | DOAJ |
| description | The search for the right person for the right job, or in other words the selection of the candidate who best reflects the skills demanded by employers to perform a specific set of duties in a job appointment, is a key premise of the personnel selection pipeline of recruitment departments. This task is usually performed by human experts who examine the résumé or curriculum vitae of candidates in search of the right skills necessary to fit the vacant position. Recent advances in AI, specifically in the fields of text analytics and natural language processing, have sparked the interest of research on the application of these technologies to help recruiters accomplish this task or part of it automatically, applying algorithms for information extraction, parsing, representation, and matching of résumés and job descriptions, or sections within. In this study, we aim to better understand how the research landscape in this field has evolved. To do this, we follow a multifaceted bibliometric approach aimed at identifying trends, dynamics, structures, and visual mapping of the most relevant topics, highly cited or influential papers, authors, and universities working on these topics, based on a publication record retrieved from Scopus and Google Scholar bibliographic databases. We conclude that, unlike a traditional literature review, the bibliometric-guided approach allowed us to discover a more comprehensive picture of the evolution of research in this subject and to clearly identify paradigm shifts from the earliest stages to the most recent efforts proposed to address this problem. |
| format | Article |
| id | doaj-art-9d4534eb8bee4d1083f36a80fdc5a0f5 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-9d4534eb8bee4d1083f36a80fdc5a0f52025-02-03T06:13:35ZengWileyThe Scientific World Journal1537-744X2022-01-01202210.1155/2022/8002363A Bibliometric Perspective on AI Research for Job-Résumé MatchingSergio Rojas-Galeano0Jorge Posada1Esteban Ordoñez2Universidad Distrital Francisco José de CaldasNatura SoftwareNatura SoftwareThe search for the right person for the right job, or in other words the selection of the candidate who best reflects the skills demanded by employers to perform a specific set of duties in a job appointment, is a key premise of the personnel selection pipeline of recruitment departments. This task is usually performed by human experts who examine the résumé or curriculum vitae of candidates in search of the right skills necessary to fit the vacant position. Recent advances in AI, specifically in the fields of text analytics and natural language processing, have sparked the interest of research on the application of these technologies to help recruiters accomplish this task or part of it automatically, applying algorithms for information extraction, parsing, representation, and matching of résumés and job descriptions, or sections within. In this study, we aim to better understand how the research landscape in this field has evolved. To do this, we follow a multifaceted bibliometric approach aimed at identifying trends, dynamics, structures, and visual mapping of the most relevant topics, highly cited or influential papers, authors, and universities working on these topics, based on a publication record retrieved from Scopus and Google Scholar bibliographic databases. We conclude that, unlike a traditional literature review, the bibliometric-guided approach allowed us to discover a more comprehensive picture of the evolution of research in this subject and to clearly identify paradigm shifts from the earliest stages to the most recent efforts proposed to address this problem.http://dx.doi.org/10.1155/2022/8002363 |
| spellingShingle | Sergio Rojas-Galeano Jorge Posada Esteban Ordoñez A Bibliometric Perspective on AI Research for Job-Résumé Matching The Scientific World Journal |
| title | A Bibliometric Perspective on AI Research for Job-Résumé Matching |
| title_full | A Bibliometric Perspective on AI Research for Job-Résumé Matching |
| title_fullStr | A Bibliometric Perspective on AI Research for Job-Résumé Matching |
| title_full_unstemmed | A Bibliometric Perspective on AI Research for Job-Résumé Matching |
| title_short | A Bibliometric Perspective on AI Research for Job-Résumé Matching |
| title_sort | bibliometric perspective on ai research for job resume matching |
| url | http://dx.doi.org/10.1155/2022/8002363 |
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