Vol. 2 No. 7 (2025)

This issue explores intelligent recruitment optimization in the hospitality sector through large language model-based resume-job matching algorithms, featuring pioneering research on position-specific adaptability in hotel industry hiring. The study addresses critical challenges in hotel recruitment where diverse talent requirements across different positions render traditional resume screening methods insufficient for complex matching demands. The research integrates LoRA fine-tuning techniques with BERT model domain adaptation and develops a multi-dimensional matching algorithm encompassing skills, experience, and soft skills evaluation. Using a comprehensive dataset of 1,847 historical recruitment records from 2014-2024 across six major hotel departments, experimental validation reveals significant performance variations: highly standardized positions achieve superior matching accuracy (front desk: 89.2%, housekeeping: 87.6%) compared to personalized roles (sales manager: 76.8%, F&B supervisor: 78.1%). The system demonstrates a remarkable 32.1 percentage point improvement in F1 score over traditional TF-IDF methods with statistical significance. This research showcases artificial intelligence's transformative potential in human resources management, providing practical solutions for intelligent recruitment systems and automated talent matching in service-intensive industries.