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- Title
Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process.
- Authors
Kurek, Jarosław; Latkowski, Tomasz; Bukowski, Michał; Świderski, Bartosz; Łępicki, Mateusz; Baranik, Grzegorz; Nowak, Bogusz; Zakowicz, Robert; Dobrakowski, Łukasz
- Abstract
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.
- Subjects
ARTIFICIAL intelligence; JOB qualifications; JOB descriptions; LABOR market
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 6, p2601
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14062601