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Design of Human Resource Management System Based on Deep Learning

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resources information such as talent information and postinformation has also increased unprecedentedly, which...

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Detalles Bibliográficos
Autores principales: Li, Jinlong, Zhou, Zhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329010/
https://www.ncbi.nlm.nih.gov/pubmed/35909850
http://dx.doi.org/10.1155/2022/9122881
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author Li, Jinlong
Zhou, Zhe
author_facet Li, Jinlong
Zhou, Zhe
author_sort Li, Jinlong
collection PubMed
description With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resources information such as talent information and postinformation has also increased unprecedentedly, which makes human resources services face the problem of information overload. At the same time, deep learning has achieved great success in a series of fields such as computer vision, natural language processing, and semantic recognition in recent years. However, there are few related works in the field of deep learning applied to human resource management system at present. Therefore, this paper studies and improves the recommendation algorithm based on deep learning and applies it to the field of human resources recommendation. In order to improve the traditional and single algorithm of the existing recommendation system, and improve the performance of the human resource management recommendation system.
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spelling pubmed-93290102022-07-28 Design of Human Resource Management System Based on Deep Learning Li, Jinlong Zhou, Zhe Comput Intell Neurosci Research Article With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resources information such as talent information and postinformation has also increased unprecedentedly, which makes human resources services face the problem of information overload. At the same time, deep learning has achieved great success in a series of fields such as computer vision, natural language processing, and semantic recognition in recent years. However, there are few related works in the field of deep learning applied to human resource management system at present. Therefore, this paper studies and improves the recommendation algorithm based on deep learning and applies it to the field of human resources recommendation. In order to improve the traditional and single algorithm of the existing recommendation system, and improve the performance of the human resource management recommendation system. Hindawi 2022-07-20 /pmc/articles/PMC9329010/ /pubmed/35909850 http://dx.doi.org/10.1155/2022/9122881 Text en Copyright © 2022 Jinlong Li and Zhe Zhou. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Jinlong
Zhou, Zhe
Design of Human Resource Management System Based on Deep Learning
title Design of Human Resource Management System Based on Deep Learning
title_full Design of Human Resource Management System Based on Deep Learning
title_fullStr Design of Human Resource Management System Based on Deep Learning
title_full_unstemmed Design of Human Resource Management System Based on Deep Learning
title_short Design of Human Resource Management System Based on Deep Learning
title_sort design of human resource management system based on deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329010/
https://www.ncbi.nlm.nih.gov/pubmed/35909850
http://dx.doi.org/10.1155/2022/9122881
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