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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Vydáno v:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Ročník 3; s. 97 - 101
Hlavní autoři: Yuanyuan Wang, Chan, S. C-F, Ngai, G.
Médium: Konferenční příspěvek
Jazyk:angličtina
japonština
Vydáno: IEEE 01.12.2012
Témata:
demographic recommender
machine learning
tourism
ISBN:9781467360579, 1467360570
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Shrnutí: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