Machine Learning in Human Resource Management

At present, data is in a state of explosive growth. The rapid growth of data collected by enterprises has exceeded the processing capacity of traditional human resource management systems, resulting in their inability to perform data management and data analysis. In order to improve the practicality of the human resource management system, this paper applies machine learning technology to the human resource management system, selects dimensions according to the prediction method, and builds a combined model consisting of an optimized GM (1,1) model and a BP neural network model. The model is implemented by a three-layer BP neural network. In order to verify the performance of the research model, this article conducts research using an entity as an example. The research results show that the method proposed in this paper has certain practical effects and can improve the reference for subsequent related research.

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Use of Artificial Intelligence in Human Resource Management: “Application of Machine Learning Algorithms to an Intelligent Recruitment System”

Lecture Notes in Networks and Systems - Advances in Deep Learning, Artificial Intelligence and Robotics ◽

10.1007/978-3-030-85365-5_20 ◽

2022 ◽

pp. 203-215

Author(s):

Said Achchab ◽

Yassine Khallouk Temsamani

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Resource Management ◽

Human Resource Management ◽

Human Resource ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Management Application ◽

Recruitment System

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A review of machine learning applications in human resource management

International Journal of Productivity and Performance Management ◽

10.1108/ijppm-08-2020-0427 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Swati Garg ◽

Shuchi Sinha ◽

Arpan Kumar Kar ◽

Mauricio Mani

Keyword(s):

Machine Learning ◽

Resource Management ◽

Human Resource Management ◽

Performance Management ◽

Human Resource ◽

Systematic Approach ◽

Early Stage ◽

Theoretical Frameworks ◽

Content Type ◽

And Performance

PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

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