Personalized Landmark Recommendation for Language-Specific Users by Open Data Mining

Siya Bao*, Masao Yanagisawa, Nozomu Togawa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


This paper proposes a personalized landmark recommendation algorithm aiming at exploring new sights into the determinants of landmark satisfaction prediction. We gather 1,219,048 user-generated comments in Tokyo, Shanghai and New York from four travel websites. We find that users have diverse satisfaction on landmarks those findings, we propose an effective algorithm for personalize landmark satisfaction prediction. Our algorithm provides the top-6 landmarks with the highest satisfaction to users for a one-day trip plan our proposed algorithm has better performances than previous studies from the viewpoints of landmark recommendation and landmark satisfaction prediction.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages15
Publication statusPublished - 2019

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • Landmark recommendation
  • Landmark satisfaction prediction
  • User-generated comment

ASJC Scopus subject areas

  • Artificial Intelligence


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