References
- A. Khan, N. Khan, and M. Shafiq, “The Economic Impact of COVID-19 from a Global Perspective,” Contemporary Economics, vol. 15, no. 1, pp. 64–75, 2021. https://doi.org/10.5709/ce.1897-9254.436
- E. Saluveer et al., “Methodological framework for producing national tourism statistics from mobile positioning data,” Annals of Tourism Research, vol. 81, Mar. 2020, Art. no. 102895. https://doi.org/10.1016/j.annals.2020.102895
- N. Comerio and F. Strozzi, “Tourism and its economic impact: A literature review using bibliometric tools,” Tourism Economics, vol. 25, no. 1, pp. 109–131, Aug. 2018. https://doi.org/10.1177/1354816618793762
- H. Herath and M. Mittal, “Adoption of artificial intelligence in smart cities: A comprehensive review,” International Journal of Information Management Data Insights, vol. 2, no. 1, Apr. 2022, Art. no. 100076. https://doi.org/10.1016/j.jjimei.2022.100076
- J. Bulchand-Gidumal, “Impact of artificial intelligence in travel, tourism, and hospitality,” in Handbook of e-Tourism, Z. Xiang, M. Fuchs, U. Gretzel, and W. Höpken, Eds. Springer, Cham, 2022, pp. 1943–1962. https://doi.org/10.1007/978-3-030-48652-5_110
- I. Tussyadiah and G. Miller, “Perceived impacts of artificial intelligence and responses to positive behaviour change intervention,” in Information and Communication Technologies in Tourism 2019, J. Pesonen and J. Neidhardt, Eds. Springer, Cham, Dec. 2018, pp. 359–370. https://doi.org/10.1007/978-3-030-05940-8_28
- A. Tlili, B. Shehata, M. A. Adarkwah, A. Bozkurt, D. T. Hickey, R. Huang, and B. Agyemang, “What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education,” Smart Learning Environments, vol. 10, Feb. 2023, Art. no. 15. https://doi.org/10.1186/s40561-023-00237-x
- J. Wirtz, P. G. Patterson, W. H. Kunz, T. Gruber, V. N. Lu, S. Paluch, and A. Martins, “Brave new world: service robots in the frontline,” Journal of Service Management, vol. 29, no. 5, pp. 907–931, Sept. 2018. https://doi.org/10.1108/JOSM-04-2018-0119
- F. Wotawa, I.-D. Nica, and O. Tazl, “Chatbot-based tourist recommendations using model-based reasoning,” in 20th International Workshop on Configuration, ConfWS, 2018. [Online]. Available: https://ceur-ws.org/Vol-2220/05_CONFWS18_paper_31.pdf
- D. C. Ukpabi, B. Aslam, and H. Karjaluoto, “Chatbot adoption in tourism services: A conceptual exploration,” in Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality, Emerald Publishing Ltd., Oct. 2019, pp. 105–121. https://doi.org/10.1108/978-1-78756-687-320191006
- L. Li, K. Y. Lee, E. Emokpae, and S.-B. Yang, “What makes you continuously use chatbot services? evidence from Chinese online travel agencies,” Electronic Markets, vol. 31, pp. 575–599, Jan. 2021. https://doi.org/10.1007/s12525-020-00454-z
- Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48–62, Sep. 2021. https://doi.org/10.1016/j.neucom.2021.03.091
- Z. Fan et al., “Mask attention networks: Rethinking and strengthen transformer,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun. 2021, pp. 1692–1701. https://doi.org/10.18653/v1/2021.naacl-main.135
- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, Minneapolis, Minnesota, Jun. 2018, pp. 4171–4186. https://aclanthology.org/N19-1423.pdf
- Y. Liu et al., “Roberta: A robustly optimized Bert pretraining approach,” arXiv:1907.11692, Jul. 2019. https://doi.org/10.48550/arXiv.1907.11692
- T. Bocklisch, J. Faulkner, N. Pawlowski, and A. Nichol, “Rasa: Open source language understanding and dialogue management,” in NIPS Conversational AI Workshop, CA, USA, 2017. https://arxiv.org/pdf/1712.05181.pdf
- Q. Ye, Z. Zhang, and R. Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches,” Expert Systems with Applications, vol. 36, no. 3, pp. 6527–6535, Apr. 2009. https://doi.org/10.1016/j.eswa.2008.07.035
- M. Nilashi, K. Bagherifard, M. Rahmani, and V. Rafe, “A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques,” Computers & Industrial Engineering, vol. 109, pp. 357–368, 2017. https://doi.org/10.1016/j.cie.2017.05.016
- Z. Abbasi-Moud, H. Vahdat-Nejad, and J. Sadri, “Tourism recommendation system based on semantic clustering and sentiment analysis,” Expert Systems with Applications, vol. 167, Apr. 2021, Art. no. 114324. https://doi.org/10.1016/j.eswa.2020.114324
- R. Colomo-Palacios, F. J. García-Peñalvo, V. Stantchev, and S. Misra, “Towards a social and context-aware mobile recommendation system for tourism,” Pervasive and Mobile Computing, vol. 38, no. 2, pp. 505–515, July 2017. https://doi.org/10.1016/j.pmcj.2016.03.001
- L. Chen, L. Zhang, S. Cao, Z. Wu, and J. Cao, “Personalized itinerary recommendation: Deep and collaborative learning with textual information,” Expert Systems with Applications, vol. 144, Apr. 2020, Art. no. 113070. https://doi.org/10.1016/j.eswa.2019.113070
- R. Anacleto, L. Figueiredo, A. Almeida, and P. Novais, “Mobile application to provide personalized sightseeing tours,” Journal of Network and Computer Applications, vol. 41, pp. 56–64, May 2014. https://doi.org/10.1016/j.jnca.2013.10.005
- P.-J. Lee, Y.-H. Hu, and K.-T. Lu, “Assessing the helpfulness of online hotel reviews: A classification-based approach,” Telematics and Informatics, vol. 35, no. 2, pp. 436–445, May 2018. https://doi.org/10.1016/j.tele.2018.01.001
- A. Aebli, “Tourists’ motives for gamified technology use,” Annals of Tourism Research, vol. 78, Sep. 2019, Art. no. 102753. https://doi.org/10.1016/j.annals.2019.102753
- I. D. Wahyono, K. Asfani, M. M. Mohamad, A. Aripriharta, A. P. Wibawa, and W. Wibisono, “New smart map for tourism using artificial intelligence,” in 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Malang, Indonesia, Aug. 2020, pp. 213–216. https://doi.org/10.1109/EECCIS49483.2020.9263435
- M. Xu, “Research on smart tourism system based on artificial intelligence,” in 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, May 2023, pp. 201–205. https://doi.org/10.1109/ICIBA56860.2023.10165293
- P. Wang and J. Shao, “Escaping loneliness through tourist-chatbot interactions,” in Information and Communication Technologies in Tourism 2022, J. L. Stienmetz, B. Ferrer-Rosell, and D. Massimo, Eds. Springer, Cham, 2022, pp. 473–485. https://doi.org/10.1007/978-3-030-94751-4_44
- G. Sperlí, “A cultural heritage framework using a deep learning based chatbot for supporting tourist journey,” Expert Systems with Applications, vol. 183, Nov. 2021, Art. no. 115277. https://doi.org/10.1016/j.eswa.2021.115277
- A. Argal, S. Gupta, A. Modi, P. Pandey, S. Shim, and C. Choo, “Intelligent travel chatbot for predictive recommendation in echo platform,” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Jan. 2018, pp. 176–183. https://doi.org/10.1109/CCWC.2018.8301732
- D. S. Maylawati et al., “Chatbot for virtual travel assistant with random forest and rapid automatic keyword extraction,” in 2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED), Sukabumi, Indonesia, Aug. 2021, pp. 1–6. https://doi.org/10.1109/ICCED53389.2021.9664876
- H. K. Ahmed and J. A. Hussein, “Design and implementation of a chatbot for Kurdish language speakers using Chatfuel platform,” Kurdistan Journal of Applied Research, vol. 5, no. 2, pp. 117–135, Dec. 2020. https://doi.org/10.24017/science.2020.2.10
- R. Goel, T. Singh, S. K. Baral, S. L. Sahdev, and S. Gupta, “The era of artificial intelligence reforming tourism industry in society 5.0,” in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, Oct. 2022, pp. 1–4. https://doi.org/10.1109/ICRITO56286.2022.9964947
- B. Momjian, PostgreSQL: Introduction and Concepts, vol. 192. Addison-Wesley New York, 2001.
- X. Chen, Z. Ji, Y. Fan, and Y. Zhan, “Restful API architecture based on laravel framework,” Journal of Physics: Conference Series, vol. 910, Oct. 2017, Art. no. 012016. https://doi.org/10.1088/1742-6596/910/1/012016
- S. Axelbrooke, “Customer support on Twitter,” 2017. [Online]. Available: https://www.kaggle.com/datasets/thoughtvector/customer-support-on-twitter?trk=public_profile_project-title
- H. L. Vu, K. T. W. Ng, A. Richter, and C. An, “Analysis of input set characteristics and variances on k-fold cross validation for a recurrent neural network model on waste disposal rate estimation,” Journal of Environmental Management, vol. 311, Jun. 2022, Art. no. 114869. https://doi.org/10.1016/j.jenvman.2022.114869
- Z. Wang, Y. Lei, H. Cui, H. Miao, D. Zhang, Z. Wu, and G. Liu, “Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based k-fold cross-validation and its application for the stiffened cylindrical shells,” Aerospace Science and Technology, vol. 124, May 2022, Art.no. 107534. https://doi.org/10.1016/j.ast.2022.107534
- X. Zhang and C.-A. Liu, “Model averaging prediction by k-fold cross-validation,” Journal of Econometrics, Feb. 2022. https://doi.org/10.2139/ssrn.4032249
- J. Roy and S. Saha, “Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach,” Artificial Intelligence in Geosciences, vol. 3, pp. 28–45, Dec. 2022. https://doi.org/10.1016/j.aiig.2022.07.001
- T. R. Shultz et al., “Confusion matrix,” in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds. Springer, Boston, MA, 2011. https://doi.org/10.1007/978-0-387-30164-8_157