Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
    EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3628-3637
    Number of pages10
    ISBN (Electronic)9781538650356
    DOIs
    Publication statusPublished - 2019 Jan 22
    Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
    Duration: 2018 Dec 102018 Dec 13

    Publication series

    NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

    Conference

    Conference2018 IEEE International Conference on Big Data, Big Data 2018
    CountryUnited States
    CitySeattle
    Period18/12/1018/12/13

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    Recommender systems

    Keywords

    • language-specific
    • peak-off season detection
    • seasonal activity
    • travel distribution

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems

    Cite this

    Bao, S., Yanagisawa, M., & Togawa, N. (2019). Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, ... X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 3628-3637). [8622103] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622103

    Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. / Bao, Siya; Yanagisawa, Masao; Togawa, Nozomu.

    Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3628-3637 8622103 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Bao, S, Yanagisawa, M & Togawa, N 2019, Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622103, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3628-3637, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 18/12/10. https://doi.org/10.1109/BigData.2018.8622103
    Bao S, Yanagisawa M, Togawa N. Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. In Song Y, Liu B, Lee K, Abe N, Pu C, Qiao M, Ahmed N, Kossmann D, Saltz J, Tang J, He J, Liu H, Hu X, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3628-3637. 8622103. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). https://doi.org/10.1109/BigData.2018.8622103
    Bao, Siya ; Yanagisawa, Masao ; Togawa, Nozomu. / Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Yang Song ; Bing Liu ; Kisung Lee ; Naoki Abe ; Calton Pu ; Mu Qiao ; Nesreen Ahmed ; Donald Kossmann ; Jeffrey Saltz ; Jiliang Tang ; Jingrui He ; Huan Liu ; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3628-3637 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).
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    title = "Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis",
    abstract = "Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.",
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    N2 - Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.

    AB - Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.

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