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Emphasizing the Digital Shift of Hospitality Towards Hyper-Personalization: Application of Machine Learning Clustering Algorithms to Analyze Travelers

Emphasizing the Digital Shift of Hospitality Towards Hyper-Personalization: Application of Machine Learning Clustering Algorithms to Analyze Travelers
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Author(s): Nuno Gustavo (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Elliot Mbunge (University of Eswatini, Eswatini), Miguel Belo (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Stephen Gbenga Fashoto (University of Eswatini, Eswatini), João Miguel Pronto (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Andile Simphiwe Metfula (University of Eswatini, Eswatini), Luísa Cagica Carvalho (Instituto Politécnico de Setúbal, Portugal), Boluwaji Ade Akinnuwesi (University of Swaziland, Swaziland) and Tonderai Robson Chiremba (University of Swaziland, Swaziland)
Copyright: 2022
Pages: 19
Source title: Optimizing Digital Solutions for Hyper-Personalization in Tourism and Hospitality
Source Author(s)/Editor(s): Nuno Gustavo (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), João Pronto (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Luísa Carvalho (Polytechnic Institute of Setúbal, Portugal & and CEFAGE, University of Évora, Portugal) and Miguel Belo (Lisbon University, Portugal & Portuguese National Funding Agency for Science, Research and Technology, Portugal)
DOI: 10.4018/978-1-7998-8306-7.ch001

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Abstract

This chapter aims to review the tech evolution in hospitality, from services to eServices, that will provide hyper-personalization in the hospitality field. In the past, the services were provided by hotels through diligent staff and supported by standardized and weak technology that was not allowed to provide personalized services by itself. Therefore, the study applied K-means and FCM clustering algorithms to cluster online travelers' reviews from TripAdvisor. The study shows that K-means clustering outperforms fuzzy c-means in this study in terms of accuracy and execution time while fuzzy c-means converge faster than K-means clustering in terms of the number of iterations. K-means achieved 93.4% accuracy, and fuzzy c-means recorded 91.3% accuracy.

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