Machine learning project: understanding hospitality as a competitive differential in restaurant management
DOI:
https://doi.org/10.5585/gep.v11i3.18748Keywords:
Software Design, Naïve Bayes, Machine Learning, Hospitality in Service Competitiveness, Food and Beverage ManagementAbstract
The aim of this article is to present the development of a Machine Learning project to predict the classification of the customer in relation to the restaurant, thus enabling the use of Hospitality as a competitive differential. To achieve the objective, a Machine Learning project was developed, which involved the development of a script in the R language, which allows analysis and application in Restaurants, in order to support managers in decision-making and eventual actions to mitigate problems. In order to capture the experts' experience, a model was developed by applying the Naïve Bayes algorithm, which was trained using data obtained from the TripAdvisor Site, reaching a hit rate of around 84% with the test data. This value is acceptable for new analyzes with data from customer opinions, thus demonstrating that the project has achieved its objective.
References
ABRASEL. (2019). Home. Abrasel. https://abrasel.com.br/
Arora, N., & Kaur, P. D. (2020). A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment. Applied Soft Computing, 86, 105936. https://doi.org/10.1016/j.asoc.2019.105936
Asafu-Adjei, J. K., & Betensky, R. A. (2015). A Pairwise Naïve Bayes Approach to Bayesian Classification. International Journal of Pattern Recognition and Artificial Intelligence, 29(07), 1550023. https://doi.org/10.1142/S0218001415500238
Aulia, A., Jeong, D., Saaid, I. M., Kania, D., Shuker, M. T., & El-Khatib, N. A. (2019). A Random Forests-based sensitivity analysis framework for assisted history matching. Journal of Petroleum Science and Engineering, 181, 106237. https://doi.org/10.1016/j.petrol.2019.106237
Babbie, E. R. (1999). Metodos De Pesquisas De Survey.
Brotherton, B., & Wood, R. C. (2001). Hospitality and hospitality management. In In search of hospitality (pp. 134-156). Butterworth-Heinemann.
Brotherton, B., & Wood, R. C. (2008). The nature and meanings of “hospitality.”. The SAGE handbook of hospitality management, 37-61.
Chen, C., Zhang, G., Yang, J., Milton, J. C., & Alcántara, A. “Dely”. (2016). An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier. Accident Analysis & Prevention, 90, 95–107. https://doi.org/10.1016/j.aap.2016.02.002
Chen, W., Zhang, S., Li, R., & Shahabi, H. (2018). Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Science of The Total Environment, 644, 1006–1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
Demo, P. (2000). Metodologia do Conhecimento Científico (Edição: 1a). Atlas.
Drucker, P. F. (2000). Desafios Gerenciais Para o Século XXI (1a edição). Cengage.
Fonseca, M. T. (2018). Tecnologias gerenciais de restaurantes. Senac.
Gil, A. C. (2008). Métodos e Técnicas de Pesquisa Social (Edição: 6a). Atlas.
Godbout, J. T. (2019). To receive is to give. Revue du MAUSS, (1), 159-174.
Jiang, L., Cai, Z., Zhang, H., & Wang, D. (2013). Naive Bayes text classifiers: A locally weighted learning approach. Journal of Experimental & Theoretical Artificial Intelligence, 25(2), 273–286. https://doi.org/10.1080/0952813X.2012.721010
Jones, P. (Ed.). (2002). Introduction to hospitality operations: An indispensable guide to the industry. Cengage Learning EMEA.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Karandikar, J., McLeay, T., Turner, S., & Schmitz, T. (2015). Tool wear monitoring using naïve Bayes classifiers. The International Journal of Advanced Manufacturing Technology, 77(9–12), 1613–1626. https://doi.org/10.1007/s00170-014-6560-6
Kerlinger, F. N. (1988). Metodologia da Pesquisa em Ciências Sociais. Um Tratamento Conceitual (Edição: 8a). EPU.
King, C. A. (1995). What is hospitality? International Journal of Hospitality Management, 14(3-4), 219-234.
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716–742. https://doi.org/10.3846/tede.2019.8740
Kubat, M. (2017). An Introduction to Machine Learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0
Lashley, C. (2004). Em busca da hospitalidade: Perspectivas para um mundo globalizado (1a edição). Editora Manole.
Lashley, C. (2015). Hospitality and hospitableness. Research in Hospitality Management, 5(1), 1-7.
Lashley, C., & Morrison, A. (2010). In search of hospitality. Routledge.
Lashley, C., Lynch, P., & Morrison, A. (2007). Hospitality: an introduction. In Hospitality: A social lens (pp. 15-30). Routledge.
Li, C., Jiang, L., & Li, H. (2014). Naive Bayes for value difference metric. Frontiers of Computer Science, 8(2), 255–264. https://doi.org/10.1007/s11704-014-3038-5
Link, C. P., Silva, G., Barichello, R., & Magro, C. B. D. (2020). Fatores críticos no gerenciamento de projetos públicos sustentáveis. Revista de Gestão e Projetos, 11(2), 87–109. https://doi.org/10.5585/gep.v11i2.17490
López-Cruz, P. L., Bielza, C., & Larrañaga, P. (2015). Directional naive Bayes classifiers. Pattern Analysis and Applications, 18(2), 225–246. https://doi.org/10.1007/s10044-013-0340-z
Marconi, M. de A., & Lakatos, E. M. (2017). Metodologia científica (Edição: 7). Atlas.
Mullins, L. J. (2001). Hospitality management and organisational behaviour. Pearson Education.
Pitt-Rivers, J. (2017). From Hospitality to Grace: A Julian Pitt-Rivers Omnibus. Hau Books.
Powers, T. F., & Barrows, C. W. (2003). Introduction to the hospitality industry. John Wiley & Sons.
Prado, E. P. V., Mancini, M., & Grotta, A. (2020). Estilo de liderança e sucesso em projetos de TI em organizações brasileiras. Revista de Gestão e Projetos, 11(2), 64–86. https://doi.org/10.5585/gep.v11i2.17387
R Foundation. (2019). R: The R Foundation. https://www.r-project.org/foundation/
Recknagel, F. (2001). Applications of machine learning to ecological modelling. Ecological Modelling, 146(1–3), 303–310. https://doi.org/10.1016/S0304-3800(01)00316-7
Russo, R. de F. S. M., & Silva, L. F. da. (2019). Critérios de sucesso e fatores de sucesso: É crítico distinguir o significado de ambos. Revista de Gestão e Projetos, 10(2), Article 2. https://doi.org/10.5585/gep.v10i2.14614
Sánchez-Franco, M. J., Navarro-García, A., & Rondán-Cataluña, F. J. (2019). A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal of Business Research, 101, 499–506. https://doi.org/10.1016/j.jbusres.2018.12.051
Santos, A., Mendes, S., & Corte-Real, J. (2014). siC notícias online. (sicnoticias. sapo. pt/pais/2014/01/07/varios-restaurantes-e-baresnas-praias-destruidos-na-marginal-de-cascais). Last access on January, 7.
SEBRAE. (2019). Sebrae SÃO PAULO SP - Sebrae. https://www.sebrae.com.br/sites/PortalSebrae/ufs/sp?codUf=26
Sharma, D., Kumar, B., & Chand, S. (2019). A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test. International Journal of Intelligent Systems and Applications, 11(2), 70–82. https://doi.org/10.5815/ijisa.2019.02.08
Shiri Harzevili, N., & Alizadeh, S. H. (2018). Mixture of latent multinomial naive Bayes classifier. Applied Soft Computing, 69, 516–527. https://doi.org/10.1016/j.asoc.2018.04.020
Silver, D. L., Yang, Q., & Li, L. (2013, março 15). Lifelong Machine Learning Systems: Beyond Learning Algorithms. 2013 AAAI Spring Symposium Series. 2013 AAAI Spring Symposium Series. https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/view/5802
Skiena, S. S. (2017). The Data Science Design Manual. Springer International Publishing. https://doi.org/10.1007/978-3-319-55444-0
Spolon, A. P. G. (2015). Um exercício de hospitalidade. Revista Hospitalidade, 3-8.
Tideman, M. C. (1983). External influences on the hospitality industry. The management of hospitality., 1-23.
Villani, J., Schully, S. D., Meyer, P., Myles, R. L., Lee, J. A., Murray, D. M., & Vargas, A. J. (2018). A Machine Learning Approach to Identify NIH-Funded Applied Prevention Research. American Journal of Preventive Medicine, 55(6), 926–931. https://doi.org/10.1016/j.amepre.2018.07.024
Vural, M. S., & Gök, M. (2017). Criminal prediction using Naive Bayes theory. Neural Computing and Applications, 28(9), 2581–2592. https://doi.org/10.1007/s00521-016-2205-z
Walker, J. R. (2002). Introduction to hospitality. Prentice Hall.
Wang, S., Jiang, L., & Li, C. (2015). Adapting naive Bayes tree for text classification. Knowledge and Information Systems, 44(1), 77–89. https://doi.org/10.1007/s10115-014-0746-y
Wen, S., Wang, C., Li, H., & Zheng, G. (2019). Parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm and its realisation on Hadoop for big data. International Journal of Information Technology and Management, 18(2–3), 129–142. https://doi.org/10.1504/IJITM.2019.099818
Wood, R. C. (1992). Hospitality industry labour trends: British and international experience. Tourism Management, 13(3), 297-304.
Xu, S. (2018). Bayesian Naïve Bayes classifiers to text classification. Journal of Information Science, 44(1), 48–59. https://doi.org/10.1177/0165551516677946
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