Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process
In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2601 |
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| author | Jarosław Kurek Tomasz Latkowski Michał Bukowski Bartosz Świderski Mateusz Łępicki Grzegorz Baranik Bogusz Nowak Robert Zakowicz Łukasz Dobrakowski |
| author_facet | Jarosław Kurek Tomasz Latkowski Michał Bukowski Bartosz Świderski Mateusz Łępicki Grzegorz Baranik Bogusz Nowak Robert Zakowicz Łukasz Dobrakowski |
| author_sort | Jarosław Kurek |
| collection | DOAJ |
| description | In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like dot product and cosine similarity, we assessed their effectiveness in aligning job descriptions with candidate profiles. Our evaluations, based on Top-K Accuracy across various rankings, revealed a notable enhancement in matching accuracy compared to conventional methods. Specifically, the all-MiniLM-L6-v2 model with a chunk length of 768 exhibited outstanding performance, achieving a remarkable Top-1 accuracy of 3.35%, 55.45% for Top-100, and an impressive 81.11% for Top-500, establishing it as a highly effective tool for recruitment processes. This paper presents an in-depth analysis of these models, providing insights into their potential applications in real-world recruitment scenarios. Our findings highlight the capability of Zero-Shot Learning to address the dynamic requirements of the job market, offering a scalable, efficient, and adaptable solution for job–candidate matching and setting new benchmarks in recruitment efficiency. |
| first_indexed | 2024-04-24T18:35:00Z |
| format | Article |
| id | doaj.art-dd45e5169e6b45afba115b7346a39c98 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:35:00Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dd45e5169e6b45afba115b7346a39c982024-03-27T13:20:12ZengMDPI AGApplied Sciences2076-34172024-03-01146260110.3390/app14062601Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment ProcessJarosław Kurek0Tomasz Latkowski1Michał Bukowski2Bartosz Świderski3Mateusz Łępicki4Grzegorz Baranik5Bogusz Nowak6Robert Zakowicz7Łukasz Dobrakowski8Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, PolandDepartment of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, PolandDepartment of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, PolandDepartment of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, PolandAvenga IT Professionals sp. z o.o., ul. Gwiaździsta 66, 53-413 Wroclaw, PolandAvenga IT Professionals sp. z o.o., ul. Gwiaździsta 66, 53-413 Wroclaw, PolandAvenga IT Professionals sp. z o.o., ul. Gwiaździsta 66, 53-413 Wroclaw, PolandDepartment of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, PolandAvenga IT Professionals sp. z o.o., ul. Gwiaździsta 66, 53-413 Wroclaw, PolandIn the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like dot product and cosine similarity, we assessed their effectiveness in aligning job descriptions with candidate profiles. Our evaluations, based on Top-K Accuracy across various rankings, revealed a notable enhancement in matching accuracy compared to conventional methods. Specifically, the all-MiniLM-L6-v2 model with a chunk length of 768 exhibited outstanding performance, achieving a remarkable Top-1 accuracy of 3.35%, 55.45% for Top-100, and an impressive 81.11% for Top-500, establishing it as a highly effective tool for recruitment processes. This paper presents an in-depth analysis of these models, providing insights into their potential applications in real-world recruitment scenarios. Our findings highlight the capability of Zero-Shot Learning to address the dynamic requirements of the job market, offering a scalable, efficient, and adaptable solution for job–candidate matching and setting new benchmarks in recruitment efficiency.https://www.mdpi.com/2076-3417/14/6/2601recommendation modelpretrained nlp modelsjob–candidate matchingrecruitment process |
| spellingShingle | Jarosław Kurek Tomasz Latkowski Michał Bukowski Bartosz Świderski Mateusz Łępicki Grzegorz Baranik Bogusz Nowak Robert Zakowicz Łukasz Dobrakowski Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process Applied Sciences recommendation model pretrained nlp models job–candidate matching recruitment process |
| title | Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process |
| title_full | Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process |
| title_fullStr | Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process |
| title_full_unstemmed | Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process |
| title_short | Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process |
| title_sort | zero shot recommendation ai models for efficient job candidate matching in recruitment process |
| topic | recommendation model pretrained nlp models job–candidate matching recruitment process |
| url | https://www.mdpi.com/2076-3417/14/6/2601 |
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