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

  • Siya BaoEmail author
  • Masao Yanagisawa
  • Nozomu Togawa
Part of the Studies in Computational Intelligence book series (SCI, volume 791)


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.


User-generated comment Landmark satisfaction prediction Landmark recommendation 



This paper was supported in part by Grant-in-Aid for Scientific Research (No. 17K19986).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Communications EngineeringWaseda UniversityTokyoJapan

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