Abstract
Traditional methods of automatic generation of recruitment text usually rely on a large number of data annotation and complex statistical algorithms, but these methods have certain limitations. In order to further optimize the processing power of neural network and improve its computational efficiency and stability, this paper aims to solve the shortcomings of convergence and local vulnerability in existing neural network algorithms, and make general improvements to them. Through optical detection and improved neural network algorithm, a more efficient method of automatic generation of artificial intelligence recruitment text is developed. Candidates' resumes are pre-processed with light detection technology to extract key information and filter out spam text. Then the processed text is input into the improved neural network model for training to improve the quality of recruitment text generation. The experimental results show that the proposed method has achieved significant improvement in the accuracy and readability of the automatic generation of recruitment texts, which can provide high quality recruitment texts for human resources departments, improve recruitment efficiency and reduce labor costs. This study provides new ideas and methods for further developing the application of artificial intelligence in the field of recruitment.
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XH has written the first version, YH and CM has done the simulations. All authors have contributed to the paper’s analysis, discussion, writing, and revision.
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Huang, X., Huang, Y. & Mercado, C. Artificial intelligence recruitment text automatic generation based on light detection and improved neural network algorithm. Opt Quant Electron 56, 162 (2024). https://doi.org/10.1007/s11082-023-05770-0
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DOI: https://doi.org/10.1007/s11082-023-05770-0