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Title

Uncovering Hotel Guests Preferences through Data Mining Techniques

Author

Mostafa Kamalpour , Atae Rezaei Aghdam, Shuxiang Xu, Ehsan Ghasem khani Aryan Baghi

Citation

Vol. 17  No. 8  pp. 1-10

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.

Keywords

Web Mining, Tourism, Data Mining, Hotel.

URL

http://paper.ijcsns.org/07_book/201708/20170801.pdf