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

    研究成果: Conference contribution

    抄録

    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.

    元の言語English
    ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
    編集者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
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ3628-3637
    ページ数10
    ISBN(電子版)9781538650356
    DOI
    出版物ステータスPublished - 2019 1 22
    イベント2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
    継続期間: 2018 12 102018 12 13

    出版物シリーズ

    名前Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

    Conference

    Conference2018 IEEE International Conference on Big Data, Big Data 2018
    United States
    Seattle
    期間18/12/1018/12/13

    Fingerprint

    Recommender systems

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems

    これを引用

    Bao, S., Yanagisawa, M., & Togawa, N. (2019). Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. : 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 (版), 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. 版 / 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).

    研究成果: Conference contribution

    Bao, S, Yanagisawa, M & Togawa, N 2019, Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. : 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 (版), 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. : 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, 編集者, 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. 編集者 / 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).
    @inproceedings{f888b2327aa048cdbab4dcd8a550e522,
    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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    AU - Bao, Siya

    AU - Yanagisawa, Masao

    AU - Togawa, Nozomu

    PY - 2019/1/22

    Y1 - 2019/1/22

    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.

    KW - language-specific

    KW - peak-off season detection

    KW - seasonal activity

    KW - travel distribution

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