Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/25443
Autoria: | Camacho, P. Almeida, A. de. António, N. |
Editor: | Carvalho, J. V. de., Rocha, Á., Liberato, P., and Peña, A. |
Data: | 2020 |
Título próprio: | Using customer segmentation to build a hybrid recommendation model |
Volume: | 208 |
Paginação: | 299 - 308 |
Título do evento: | International Conference on Tourism, Technology and Systems, ICOTTS 2020 |
ISSN: | 2190-3018 |
ISBN: | 978-981-33-4256-9 |
DOI (Digital Object Identifier): | 10.1007/978-981-33-4256-9_27 |
Palavras-chave: | Hospitality Transfers Customer segmentation Recommendation system |
Resumo: | The growing trend in leisure tourism has been closely followed by the number of hospitality services. Nowadays, customers are more sophisticated and demand a personalized and simplified experience, which is commonly achieved through the use of technological means for anticipating customer behavior. Thus, the ability to predict a customer’s willingness to buy is also a growing trend in hospitality businesses to reach more customers and consolidate existing ones. The acquisition of a transfer service through website reservation generates data that can be used to perform customer segmentation and enable recommendations for other products or services to a customer, like recreation experiences. This work uses data from a Portuguese private transfer company to understand how its private transfer business customers can be segmented and how to predict their behavior to enhance services cross-selling. Information extracted from the data acquired with the private transfer reservations is used to train a model to predict customer willingness to buy, and based on it, offer leisure services to customers. For that, a hybrid classifier was trained to offer recommendations to a customer when he/she is booking a transfer. The model employs a two-phase process: first, a binary classifier asserts if the customer who’s buying the transfer would eventually buy a service experience. In that case, a multi-class model decides what should be the most likely experience to be recommended. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-CRI - Comunicações a conferências internacionais IT-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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conferenceobject_82407.pdf | Versão Aceite | 641,2 kB | Adobe PDF | Ver/Abrir |
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