Show source-provided references
1. Talluri, K.T., and van Ryzin, G.J. (2004). The Theory and Practice of Revenue Management, Springer. International Series in Operations Research & Management Science.
10.1007/b139000 ↗2. Stevenson, W.J. (2021). Operations Management, McGraw-Hill Education. [14th ed.].
6. The role of ChatGPT on customer service in the hospitality industry: An exploratory study of hospitality workers’ experiences and perceptions
10.20867/thm.29.4.9 ↗7. Vicente-Martínez, P., Soria-Olivas, E., Esteve-Mompó, I., Sánchez-Montañés, M., García Escrivà, M.Á., and William-Secin, E. (2026). Design and Evaluation of an AI-Based Conversational Agent for Travel Agencies: Enhancing Training, Assistance, and Operational Efficiency. AI, 7.
10.3390/ai7040123 ↗8. Kukanja, M., and Planinc, T. (2025). Adoption of artificial intelligence in micro and small hospitality enterprises: The role of organisational characteristics and managers’ attitudes toward AI in relation to operating revenues. Tour. Hosp., 6.
10.3390/tourhosp6050268 ↗9. Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrial-Strength Natural Language Processing in Python. Zenodo.
10. Cohen, A. (2024, October 01). FuzzyWuzzy: Fuzzy String Matching in Python. Available online: https://github.com/seatgeek/thefuzz.
13. Gendreau, M., and Potvin, J.Y. (2019). Handbook of Metaheuristics, Springer. [3rd ed.]. International Series in Operations Research & Management Science.
10.1007/978-3-319-91086-4 ↗14. A taxonomy and research overview of perishable-asset revenue management: Yield management, overbooking, and pricing
10.1287/opre.40.5.831 ↗18. Attention is all you need
19. Language models are few-shot learners
21. Meyer von Wolff, R., Hobert, S., and Schumann, M. (2019, January 8–11). How May I Help You?—State of the Art and Open Research Questions for Chatbots at the Digital Workplace. Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS 2019), Maui, HI, USA.
10.24251/HICSS.2019.013 ↗23. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. (2023, January 1–5). ReAct: Synergizing Reasoning and Acting in Language Models. Proceedings of the 11th International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda.
24. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., and Lin, Y. (2024). A Survey on Large Language Model Based Autonomous Agents. Front. Comput. Sci., 18.
10.1007/s11704-024-40231-1 ↗25. Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection, Springer. Data-Centric Systems and Applications.
10.1007/978-3-642-31164-2 ↗26. Batini, C., and Scannapieco, M. (2016). Data and Information Quality: Dimensions, Principles and Techniques, Springer. Data-Centric Systems and Applications.
10.1007/978-3-319-24106-7 ↗28. Mudgal, S., Li, H., Rekatsinas, T., Doan, A., Park, Y., Krishnan, G., Deep, R., Arcaute, E., and Raghavendra, V. (2018). Deep learning for entity matching: A design space exploration. Proceedings of the ACM SIGMOD 2018, ACM.
10.1145/3183713.3196926 ↗29. Binary codes capable of correcting deletions, insertions and reversals
30. Cohen, W.W., Ravikumar, P., and Fienberg, S.E. (2003). A comparison of string distance metrics for name-matching tasks. Proceedings of the IJCAI-2003 Workshop on Information Integration on the Web (IIWeb-2003), ACM.
31. Bilenko, M., Kamath, B., and Mooney, R.J. (2006). Adaptive blocking: Learning to scale up record linkage. Proceedings of the IEEE ICDM 2006, IEEE.
10.1109/ICDM.2006.13 ↗32. Marcus, G. (2020). The next decade in AI: Four steps towards robust artificial intelligence. arXiv.
33. National Aeronautics and Space Administration (2016). NASA Systems Engineering Handbook, Rev. 2, SP-2016-6105.
34. Bass, L., Clements, P., and Kazman, R. (2021). Software Architecture in Practice, Addison-Wesley Professional. [4th ed.].
35. Gemini Team, Google (2025). Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities. arXiv.
36. Bohnet, B., Tran, V.Q., Verga, P., Aharoni, R., Andor, D., Soares, L.B., Ciaramita, M., Eisenstein, J., Ganchev, K., and Herzig, J. (2022). Attributed question answering: Evaluation and modeling for attributed large language models. arXiv.
37. White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D.C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv.
38. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation
40. Christen, P. (2006, January 18–22). A Comparison of Personal Name Matching: Techniques and Practical Issues. Proceedings of the IEEE ICDM 2006 Workshops, Hong Kong, China.
42. Williams, J.D., Asadi, K., and Zweig, G. (August, January 30). Hybrid Code Networks: Practical and Efficient End-to-End Dialog Control with Supervised and Reinforcement Learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, BC, Canada.
10.18653/v1/P17-1062 ↗43. Amershi, S., Weld, D., and Vorvoreanu, M. (2019, January 4–9). Guidelines for Human-AI Interaction. Proceedings of the CHI 2019, Glasgow, UK.
10.1145/3290605.3300233 ↗44. Vicente-Martínez, P., Soria-Olivas, E., Sebastiá-García, S., Vizcaíno-Ramírez, C., Chust-Ros, A., García-Escrivà, M.Á., and William-Secin, E. (2026). Integrating Conversational AI Agents with Digital Twins: A Systems Engineering Approach to Complex Infrastructure Management and Predictive Decision-Making. Electronics, 15.
10.3390/electronics15091869 ↗