Applicability of demographic recommender system to tourist attractions: A case study on TripAdvisor

Yuanyuan Wang, Stephen Chi Fai Chan, Grace Ngai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

46 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012
Number of pages5
Publication statusPublished - 1 Dec 2012
Event2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China
Duration: 4 Dec 20127 Dec 2012


Conference2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012


  • demographic recommender
  • machine learning
  • tourism

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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