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Effective URL: https://ieeexplore.ieee.org/document/10788241/
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Optimizing Job Matching Efficiency: A Novel Employment Information System
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OPTIMIZING JOB MATCHING EFFICIENCY: A NOVEL EMPLOYMENT INFORMATION SYSTEM
ALGORITHM
Publisher: IEEE
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Zhu Xiuyun; Yang Hualing
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Abstract
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* I.
Introduction
* II.
Related Literature
* III.
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Experimental Test on Datasets
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ABSTRACT:
Optimizing the element of job matching and its efficiency remains fundamental in
the labor market. To this effect, a novel employment data information system
algorithm re...View more
METADATA
ABSTRACT:
Optimizing the element of job matching and its efficiency remains fundamental in
the labor market. To this effect, a novel employment data information system
algorithm remains a widely used approach in optimizing the process, through the
leveraging of machine learning models and advanced data analytics. The use of
such an algorithm focuses on a set of factors that include precise process of
matching, continuous learning, and dynamic adaptability. The algorithm primarily
relies on natural language processing (NLP) in understanding resumes and job
descriptions beyond the mere use of keyword matching. The process widely takes
consideration of semantic and context in ensuring that accurate fits are
determined. In the evaluation of experiences, qualifications, and skills, a
matched candidate to different roles is determined through the system. This
therefore justifies the systems efficiency in dynamically adapting and ensuring
that responsive measures are provided to the changing dynamics within the job
market. Algorithms are established to provide regular updates continuously
through a parameter that relies on real-time data examining the job market. The
nature of such adaptability provides an up-to date and timely recommendations to
job seekers and employers. This article therefore seeks to conduct an
examination on the optimizing of job matching efficiency through a novel
employment of information system algorithms. According to findings, information
system algorithms play a critical role in refining predictive over time, a
process that improves the accuracy of the job matching process and its
reliability.
Published in: 2024 International Conference on Computers, Information Processing
and Advanced Education (CIPAE)
Date of Conference: 26-28 August 2024
Date Added to IEEE Xplore: 18 December 2024
ISBN Information:
DOI: 10.1109/CIPAE64326.2024.00134
Publisher: IEEE
Conference Location: Ottawa, ON, Canada
Contents
--------------------------------------------------------------------------------
I. INTRODUCTION
Within the contemporary and current labor market, issues of effective matching
of work with job seekers seeking employment opportunities remains complex.
According to Brebion and Leschke [1], the traditional job matching model and
process remains widely reliant on the use of basic word searches and manual
reviews, an aspect that widely falls short of addressing the needs of both the
candidates and employers. Such inefficiencies often result in mismatched
placements, prolonged processes of job searches, and a dissatisfaction with the
process and workforce. In addressing such issues, the need for a novel
employment of systemic information systems and algorithms remains a phenomenon
that is proposed, an aspect that is intended in this article to revolutionize
the manner in which job searches are conducted. A proposed algorithm therefore
leverages the use of cutting-edge systems and technologies in machine learning
and data analytics in the optimization of the job matching process. Unlike the
conventional approach, the use of these algorithms as proposed in this article
employs the prospects of Natural Language Processing (NLP) in the analysis of
job descriptions as well as the candidates resumes, thus providing a deeper
understanding of the semantics and context rather than the reliance on simple
keyword searches.
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