Large Language Model-based Resume-Job Intelligent Matching Algorithm and Its Adaptability Case Study Across Different Positions in the Hotel Industry
DOI:
https://doi.org/10.62677/IJETAA.2507138Keywords:
Large Language Models, Resume-job matching, Hotel industry, Position adaptability, Intelligent recruitmentAbstract
As a service-intensive industry, the hotel sector exhibits significant variations in talent requirements across different positions, making traditional resume screening methods inadequate for meeting the complex matching demands of various roles. This study develops a resume-job intelligent matching system specifically designed for the hotel industry based on large language model technology and conducts an in-depth analysis of its adaptability performance across different position types. Using a chain hotel group as a case study, we collected 1,847 historical recruitment records spanning nearly 10 years (2014-2024) across six major categories: front office, housekeeping, food & beverage, sales, logistics, and management. Through controlled experiments and ablation studies, we systematically evaluated the performance differences of large language models in resume screening across various position types. The experiments employed LoRA fine-tuning techniques for domain adaptation of the BERT model and designed a multi-dimensional matching algorithm integrating skills, experience, and soft skills evaluation dimensions. Results demonstrate that the system achieves significantly higher matching accuracy in highly standardized positions (front desk reception: 89.2%, housekeeping: 87.6%) compared to positions requiring strong personalization (sales manager: 76.8%, F&B supervisor: 78.1%). Compared to traditional TF-IDF methods, the F1 score improved by 32.1 percentage points with statistical significance (p < 0.001).
Downloads
References
P. Skondras, P. Zervas, and G. Tzimas, “Generating synthetic resume data with large language models for enhanced job description classification,” Future Internet, vol. 15, no. 11, p. 363, 2023.
H. Kavas, M. Serra-Vidal, and L. Wanner, “Using large language models and recruiter expertise for optimized multilingual job offer–applicant CV matching,” in Proc. 33rd Int. Joint Conf. Artificial Intelligence, Jeju, Republic of Korea, Aug. 2024, pp. 3-9.
A. R. Vishaline, R. K. Kumar, V. V. N. S. Sai Pramod, K. V. K. Vignesh, and P. Sudheesh, “An ML-based resume screening and ranking system,” in Proc. 2021 Int. Conf. Signal Processing, Computation, Electronics, Power and Telecommunication, Karaikal, India, Jul. 2024, pp. 1-6.
S. Bharadwaj, R. Varun, P. S. Aditya, M. Nikhil, and G. C. Babu,“Resume screening using NLP and LSTM,” in Proc. 2022 Int. Conf. Inventive Computation Technologies, Lalitpur, Nepal, Jul. 2022, pp. 238-241.
D. Lavi, V. Medentsiy, and D. Graus, “ConsultantBERT: Fine-tuned siamese sentence-BERT for matching jobs and job seekers,” arXiv preprint arXiv:2109.06501, 2021.
B. Kinge, S. Mandhare, P. Chavan, and S. M. Chaware, “Resume screening using machine learning and NLP: A proposed system,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 8, no. 2, pp. 253-258, 2022.
T. M. Harsha, G. S. Moukthika, D. S. Sai, M. N. R. Pravallika, S. Anamalamudi, and M. Enduri, “Automated resume screener using natural language processing (NLP),” in Proc. 2022 6th Int. Conf. Trends in Electronics and Informatics, Coimbatore, India, Apr. 2022, pp. 1772-1777.
A. Pe˜na, I. Serna, A. Morales, J. Fierrez, A. Ortega, A. Herrarte, and J. Ortega-Garcia, “Human-centric multimodal machine learning: Recent advances and testbed on AI-based recruitment,” SN Computer Science, vol. 4, no. 5, p. 434, 2023.
I. Ali, N. Mughal, Z. H. Khand, J. Ahmed, and G. Mujtaba, “Resume classification system using natural language processing and machine learning techniques,” Mehran University Research Journal of Engineering and Technology, vol. 41, no. 1, pp. 65-79, 2022.
A. Jalili, H. Tabrizchi, J. Razmara, and A. Mosavi, “BiLSTM for resume classification,” in Proc. 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, Poprad, Slovakia, Jan. 2024, pp.519-524.
X. Yu, J. Zhang, and Z. Yu, “Confit: Improving resume-job matching using data augmentation and contrastive learning,” in Proc. 18th ACM Conf. Recommender Systems, Oct. 2024, pp. 601-611.
Z. Zheng, Z. Qiu, X. Hu, L. Wu, H. Zhu, and H. Xiong, “Generative job recommendations with large language model,” arXiv preprint arXiv:2307.02157, 2023.
E. Salakar, J. Rai, A. Salian, Y. Shah, and J. Wadmare, “Resume screening using large language models,” in Proc. 2023 6th Int. Conf. Advances in Science and Technology, Karaikal, India, Dec. 2023, pp.494-499.
S. Haryan, R. Malik, P. Redij, and S. Kulkarni, “FairHire: A fair and automated candidate screening system,” in Int. Conf. Machine Intelligence, Tools, and Applications, Springer, 2024, pp. 372-382.
P. Ghosh and V. Sadaphal, “JobRecoGPT–explainable job recommendations using LLMs,” arXiv preprint arXiv:2309.11805, 2023.
M. E. Erdem, “Automatic resume screening with content matching,” in Proc. 2023 8th Int. Conf. Computer Science and Engineering, Antalya, Turkey, Oct. 2023, pp. 554-558.
Y. Du, D. Luo, R. Yan, X. Wang, H. Liu, H. Zhu, Y. Song, and J. Zhang, “Enhancing job recommendation through LLM-based generative adversarial networks,” in Proc. AAAI Conf. Artificial Intelligence, vol.38, 2024, pp. 8363-8371.
A. H. AL-Qassem, K. Agha, M. Vij, H. Elrehail, and R. Agarwal, “Leading talent management: Empirical investigation on applicant tracking system (ATS) on e-recruitment performance,” in Proc. 2023 Int. Conf. Business Analytics for Technology and Security, Dubai, UAE, Mar. 2023,pp. 1-5.
A. Magron, A. Dai, M. Zhang, S. Montariol, and A. Bosselut, “JobSkape: A framework for generating synthetic job postings to enhance skill matching,” arXiv preprint arXiv:2402.03242, 2024.
L. Wang, N. Yang, X. Huang, L. Yang, R. Majumder, and F. Wei, “Multilingual E5 text embeddings: A technical report,” arXiv preprint arXiv:2402.05672, 2024.
P. Q. Wang, “Personalizing guest experience with generative AI in the hotel industry: There’s more to it than meets a Kiwi’s eye,” Current Issues in Tourism, vol. 28, no. 4, pp. 527-544, 2025.
F. P. W. Lo, J. Qiu, Z. Wang, H. Yu, Y. Chen, G. Zhang, and B. Lo, “AI hiring with LLMs: A context-aware and explainable multi-agent framework for resume screening,” in Proc. Computer Vision and Pattern Recognition Conf. , 2025, pp. 4184-4193.
M. Łepicki, T. Latkowski, I. Antoniuk, M. Bukowski, B. ´Swiderski, G. Baranik, B. Nowak, R. Zakowicz, Ł. Dobrakowski, B. Act, and J. Kurek,“Comparative evaluation of sequential neural network (GRU, LSTM, transformer) within Siamese networks for enhanced job–candidate matching in applied recruitment systems,” Applied Sciences, vol. 15, no. 11, p. 5988, 2025.
E. Albaroudi, T. Mansouri, and A. Alameer, “A comprehensive review of AI techniques for addressing algorithmic bias in job hiring,” AI, vol.5, no. 1, pp. 383-404, 2024.
Y. Mashayekhi, N. Li, B. Kang, J. Lijffijt, and T. De Bie, “A challenge-based survey of e-recruitment recommendation systems,” ACM Computing Surveys, vol. 56, no. 10, pp. 1-33, 2024.

Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Jingran Sun (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.