چکیده مقاله
This paper presents a comprehensive AI driven framework for optimizing human resource allocation through automated resume screening, classification, and Grading Utilizing advanced natural Ianguage processing NLP techniques and large language models LLMs such as GPT 3 5, the proposed method aims to enhance the efficiency and objectivity of the recruitment process We generated a diverse dataset of Perisian resumes and employed generative models to preprocess and categorize the resumes into various job specific categories Our experimental results demonstrate significant improvements in classification accuracy, grading, and summarization metrices compared to baseline models Additionally, the automated framework is shown to be approximately 10 times faster than traditional manual screening methods, highilghting its potential for widespread adoption in modern HR practices Future work will focus on further refining the model and expanding its applicability to other languages and job markets
کلیدواژهها
نویسندگان
Hamed Araghi
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Mohammad Aghdasi
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
شیوه ارجاع
Araghi, Hamed and Aghdasi, Mohammad,1403,Optimizing Human Resource Allocation Through AI-Driven Resume Classification and Grading,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
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