Applicability of Demographic Recommender System to Tourist Attractions: A Case Study on Trip Advisor

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 o...

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Bibliographic Details
Published in:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 3; pp. 97 - 101
Main Authors: Yuanyuan Wang, Chan, S. C-F, Ngai, G.
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 01.12.2012
Subjects:
demographic recommender
machine learning
tourism
ISBN:9781467360579, 1467360570
Online Access:Get full text
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Summary: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:9781467360579
1467360570
DOI:10.1109/WI-IAT.2012.133