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

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    Abstract

    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
    Pages107-121
    Number of pages15
    DOIs
    Publication statusPublished - 2019 Jan 1

    Publication series

    NameStudies in Computational Intelligence
    Volume791
    ISSN (Print)1860-949X

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    Keywords

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

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Bao, S., Yanagisawa, M., & Togawa, N. (2019). Personalized Landmark Recommendation for Language-Specific Users by Open Data Mining. In Studies in Computational Intelligence (pp. 107-121). (Studies in Computational Intelligence; Vol. 791). Springer-Verlag. https://doi.org/10.1007/978-3-319-98693-7_8