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Developing a Transferring Method for Web-click Stream Prediction based on Sequential Pattern Evaluation Indices

  • ABE Hidenao
    Faculty of Information and Communications, Bunkyo University

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Other Title
  • 系列パターン評価指標群に基づく転移型クリックストリーム予測モデル構築の検討 (人工知能と知識処理)
  • 系列パターン評価指標群に基づく転移型クリックストリーム予測モデル構築の検討
  • ケイレツ パターン ヒョウカ シヒョウグン ニ モトズク テンイガタ クリックストリーム ヨソク モデル コウチク ノ ケントウ

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Abstract

In this paper, a method for constructing transferable web-click stream prediction models based on sequential pattern evaluation indices is described. For predicting end points of click streams, the click streams are assumed as sequential data. Then, a sequential pattern generation method is applied to extract features of each click stream data. Based on these features, a classification learning algorithm is applied to construct click stream end point prediction models. In this study, evaluation indices for sequential pattern are introduced to abstract each click stream data for transferring constructed the predictive models. In the experiment, the method is applied to a benchmark click stream data to predict the end points. The result shows that the method can obtained more accurate predictive models with a decision tree learner and a classification rule learner. Subsequently, availability for transferring the predictive morels to different period is discussed.

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