Customizable Instance-Driven Webpage Filtering Based on Semi-Supervised Learning

Date

2007

Authors

Zhu, M.
Hu, W.
Li, X.
Wu, O.

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Conference paper

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2007 IEEE/WIC/ACM International Conference On Web Intelligence (WI 2007 Main Conference Proceedings): Silicon Valley, California, USA 2–5 November 2007 / T. Young, (T.Y.) Lin, L. Haas, J. Kacprzyk, R. Motwani, A. Broder & H. Ho (eds.): pp. 663-666

Statement of Responsibility

Mingliang Zhu, Weiming Hu, Xi Li and Ou Wu

Conference Name

IEEE/WIC/ACM International Conference on Web Intelligence (2007 : Silicon Valley, California)

Abstract

The World Wide Web has been growing rapidly in recent years, along with increasing needs for content-based Webpage filtering. But most existing filtering systems cannot easily satisfy the personalized filtering demands from different users at the same time. In this paper, a customizable instance-driven Webpage filtering strategy is proposed. For different users, different Webpage filters are produced by our system through mining the certain Webpage classes they focus on. A semi-supervised learning (SSL) approach is applied for obtaining a precise description of the Webpage class which a user wants to filter based on the small sized user instance set he or she provided. Subsequently, a feature selection step is performed and a Bayes classifier is created over the enlarged training set. Experimental results show the great stability and high performance of our proposed method, and it outperforms existing methods.

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© 2007 IEEE

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