Dynamic Topic Memory Networks: Time-series Behavior Prediction Based on Transition of Intrinsic Preferences

DOI

Bibliographic Information

Other Title
  • ダイナミックトピックメモリネットワーク：心的内部状態の変化を捉えた時系列行動予測

Description

<p>We predict behavior of users in the future based on past their behavior histories in order to improve the performance of location-based advertising. In the field of behavior prediction, researches using external time-series behavior history of the user has been developed. However, researches capturing the time-series transition of intrinsic preferences of the user are inadequate. Therefore, we propose the model capturing the multiple transitions of intrinsic preferences of the user, Dynamic Topic Memory Networks (DTMN). In the experiments, we predict the places where users will visit in the future. We show the effectiveness of capturing the transition of intrinsic preferences using DTMN by improving performance in comparative experiments. Through experiments, we also show the importance of capturing multiple transitions of intrinsic preferences.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390566775142639872
  • NII Article ID
    130007856671
  • DOI
    10.11517/pjsai.jsai2020.0_1i4gs203
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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