Deep Neural Network Model Construction for Digital Human Resource Management with Human-Job Matching

Area of Science:

• Artificial Intelligence
• Human Resource Management
• Data Science

Background:

• Traditional Human Resource Management (HRM) faces challenges in efficient human-job matching.
• Digital transformation offers opportunities to enhance HRM processes through advanced technologies.

Purpose of the Study:

• To systematically research human-job matching using deep neural network technology and digital HRM knowledge.
• To propose a digital transformation strategy for HRM leveraging intelligent digital means.
• To enhance job applicant support through intelligent recommendations and matching.

Main Methods:

• Utilized deep neural networks, 5G communication, cloud computing, big data, and user portrait technology.
• Developed a system using a flask framework, CSV, MySQL, and configuration files for data storage and visualization.
• Employed bidirectional long and short-term memory neural network (Bi-LSTM) and attention-based APJFNN for job recommendation.
• Implemented a joint embedded convolutional neural network (JE-CNN) for post-matching analysis.

Main Results:

• Achieved systematic human-job matching through intelligent digital means.
• Designed and implemented a digital transformation strategy for HRM.
• Provided intelligent job recommendations and improved job applicant matching accuracy.
• Quantitative analysis was used to determine job matching degrees.

Conclusions:

• Deep neural networks and digital technologies can significantly improve human-job matching in HRM.
• The proposed digital transformation strategy offers a framework for modernizing HRM practices.
• Intelligent recommendation systems enhance the job application process for seekers.