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Related Experiment Video

Updated: Sep 1, 2025

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Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application.

Jingtong Sun1,2

  • 1Department of Management and Economics, Tianjin University, Tianjin, Nankai 300100, China.

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|August 12, 2022
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Summary

The study introduces a deep learning-based human resources recommendation system to combat information overload. This system enhances personalized job and talent recommendations, overcoming the cold start problem for better user experience.

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Area of Science:

  • Computer Science
  • Human Resources Management
  • Information Retrieval

Background:

  • The internet era has led to information overload in human resources (HR), with vast amounts of job and talent data.
  • Traditional search engines struggle when users cannot clearly articulate their needs, hindering efficient data retrieval.
  • Recommender systems offer a solution to personalize information acquisition and improve user experience in HR services.

Purpose of the Study:

  • To design and implement a deep learning-based recommendation system for human resources.
  • To address the challenges of information overload and the cold start problem in HR data.
  • To enhance the quality and personalization of HR recommendations for candidates and employers.

Main Methods:

  • Developed an overall architecture for the HR recommendation system based on recommender system workflows.
  • Implemented a prototype HR recommendation system utilizing deep learning techniques.
  • Focused on overcoming the cold start problem inherent in recommendation systems.

Main Results:

  • The implemented system effectively overcomes the cold start problem.
  • The system provides real-time recommendation results for HR data.
  • Demonstrated improved quality of personalized HR recommendation results.

Conclusions:

  • Deep learning-based recommender systems offer a viable solution to information overload in HR.
  • The developed prototype system enhances user experience by providing accurate and personalized recommendations.
  • This approach improves the efficiency and effectiveness of talent and job matching.