Automatic HR Recruitment System: A MultiStage Evaluation Approach
- DOI
- 10.2991/978-94-6463-471-6_109How to use a DOI?
- Keywords
- Natural Language Processing; Machine Learning; Candidate Assessment; Technical Test
- Abstract
In response to the challenges of identifying ideal candidates in the dynamic job market, this paper introduces an Automated HR Recruitment System utilizing Natural Language Processing (NLP) and machine learning algorithms. The system automates candidate assessments, ensuring efficiency and precision in overcoming traditional recruitment hurdles. Employing a tailored technical test integrated with real-time NLP analysis expedites evaluations, guaranteeing a comprehensive and unbiased assessment of candidates’ capabilities. The system accelerates candidate screening, enhances accuracy in shortlisting, and offers an intuitive interface for easy customization of screening criteria, aligning with organizational needs. The “Automatic HR Recruitment System” stands as a transformative innovation, providing organizations with a competitive edge in identifying top talent through seamless automation and user customization.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sreerama Murthy Velaga AU - Sai Srikara Prabhas Satti AU - Sai Karthik Balivada AU - Harika Atta AU - Yaswanth Battula PY - 2024 DA - 2024/07/30 TI - Automatic HR Recruitment System: A MultiStage Evaluation Approach BT - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024) PB - Atlantis Press SP - 1143 EP - 1152 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-471-6_109 DO - 10.2991/978-94-6463-471-6_109 ID - Velaga2024 ER -