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Predicting Web Information Content

Full Text: PredictIC.ps PS

In this paper, we propose a novel method to infer the web user's Information Content(IC), i.e., the information that she must examine to complete her task. In particular, our method tries to predict which words will be in the web page that the user must examine to finish her task --- i.e., IC-page. We use page-content information extracted from the user's clickstream to train a classifier to predict what kind of words will be in the IC-page, i.e., the IC. The classifier is trained on generalized information to indicate how the user treats the information that she has visited, that is, browsing behavior. The classifier can be used to predict the IC of the web user with any given obeservable page sequence, thus it can be used in totally new environment, and to build an effective personalized system. The results indicate that our method can predict web users' IC fairly well.

Citation

T. Zhu, R. Greiner, G. Haeubl. "Predicting Web Information Content". Intelligent Techniques for Web Personalization, August 2003.

Keywords: WebIC, predicting, web user, machine learning
Category: In Workshop
Web Links: Pointer

BibTeX

@misc{Zhu+al:ITWP03,
  author = {Tingshao Zhu and Russ Greiner and Gerald Haeubl},
  title = {Predicting Web Information Content},
  Booktitle = {Intelligent Techniques for Web Personalization (IJCAI 
    Workshop)},
  booktitle = {Intelligent Techniques for Web Personalization},
  year = 2003,
}

Last Updated: April 23, 2007
Submitted by Russ Greiner

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