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Adaptive Method for Trends in Ranking of Tourist Spots

Authors:
Yusaku Takano
Masaharu Hirota
Daiju Kato
Tetsuya Araki
Masaki Endo
Hiroshi Ishikawa

Keywords: tourism; ranking learning; social recommendation

Abstract:
In recent years, for tourists deciding on a destination for a trip, demand for websites, such as TripAdvisor, for obtaining tourist information is increasing. These websites usually rank tourist spots with user reviews. Regarding tourist spots, trends appear in their popularity. When ranking tourist spots solely by reviews posted on these websites for obtaining tourist information, the possibility exists that new tourist spots and tourist spots with actively changing popularity are not ranked higher in rankings. However, a user needs to find the most enjoyable tourist spots at the time of the visit in the ranking of tourist spots. Therefore, we use tweets in this study as comments for tourist spots with high recency to generate a ranking of tourist spots considering popularity variation. Then, we model the ranking of tourist spots on TripAdvisor using tweets. After analyzing the contribution rates of classification for linguistic features and statistical features on tweets, we performed ranking of learning with effective features and generated a ranking of tourist spots considering popularity variation. Finally, our experimental results showed that tourist spots holding exhibitions or other activities frequently are ranked higher.

Pages: 13 to 19

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-690-3

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019