Please use this identifier to cite or link to this item:
Title: Applicability of demographic recommender system to tourist attractions : a case study on trip advisor
Authors: Wang, Y
Chan, S 
Ngai, G 
Keywords: Demographic recommender
Machine learning
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), 4-7 December 2012, Macau, p. 97-101 How to cite?
Abstract: Most of the existing recommender systems for tourism apply knowledge-based and content-based approaches, which need sufficient historical rating information or extra knowledge and suffer from the cold start problem. In this paper, a demographic recommender system is utilized for the recommendation of attractions. This system categorizes the tourists using their demographic information and then makes recommendations based on demographic classes. Its advantage is that the history of ratings and extra knowledge are not needed, so a new tourist can obtain recommendation. Focusing on the attractions on Trip Advisor, we use different machine learning methods to produce prediction of ratings, so as to determine whether these approaches and demographic information of tourists are suitable for providing recommendations. Our preliminary results show that the methods and demographic information can be used to predict tourists' ratings on attractions. But using demographic information alone can only achieve limited accuracy. More information such as textual reviews is required to improve the accuracy of the recommendation.
ISBN: 978-1-4673-6057-9
DOI: 10.1109/WI-IAT.2012.133
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Feb 25, 2017

Page view(s)

Last Week
Last month
Checked on Aug 20, 2017

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.