Abstract
The focus of this study is the use of Natural Language Processing (NLP) to help improve AI-based recruitment systems. The goal is to train and tune NLP models to help speed up the screening, ranking, and matching of job candidates using resumes and job descriptions. To improve recruitment chatbots, advanced models like BERT and GPT are adopted to promote dynamic candidate interaction as well as initial interviews. The study also involves constructing an NLP-driven interview simulation tool based on Hiring Manager input, create which aids ensure better candidate suitability by emulating real interactions. The research then extends the semantic search algorithm to refine the selection of candidates from large HR databases. The study seeks to address the recruitment efficiency, candidate fit and engagement challenges by integrating these methods into a more effective and streamlined hiring process.
Published Version
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