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Uncovering hotel guests preferences through data mining techniques

Citation

Kampalpour, M and Aghdam, AR and Xu, S and Khani, EG and Baghi, A, Uncovering hotel guests preferences through data mining techniques, International Journal of Computer Science and Network Security, 17, (8) pp. 1-10. ISSN 1738-7906 (2017) [Refereed Article]


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Copyright Statement

Copyright 2005 International Journal of Computer Science and Network Security

Official URL: http://paper.ijcsns.org/07_book/201708/20170801.pd...

Abstract

The proliferation of online travel communities, travel websites, and technology developments are driving tourism industry to develop new methods for marketing and improving customer satisfaction. The main aim of this study is to analyze the potential use of Data Mining and Web Mining techniques in tourism industry to extract the hidden knowledge from hotel visitors’ information. For this purpose we have collected the data, from visitors of Mersing Island hotels as found at www.tripadvisor.com through our task specific "RK" web crawler, which collected 616 user profiles information. The research method used in this research is CRISP DM, and by using this method along with "RK" Crawler, two models have been proposed to use by managers in order to improve customer satisfaction. Results show that there are a various type of tourists with each group having different preferences. For instance, if a visitor is from Singapore, male, and interested in great foods and wine, he is also interested in outdoor and adventure activities. This research study can be very helpful for tourist association, hospitality, and hotel managers.

Item Details

Item Type:Refereed Article
Keywords:web mining, tourism, data mining
Research Division:Information and Computing Sciences
Research Group:Information Systems
Research Field:Conceptual Modelling
Objective Division:Information and Communication Services
Objective Group:Information Services
Objective Field:Information Services not elsewhere classified
Author:Xu, S (Dr Shuxiang Xu)
ID Code:123797
Year Published:2017
Deposited By:Information and Communication Technology
Deposited On:2018-01-29
Last Modified:2018-06-15
Downloads:0

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