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https://hdl.handle.net/2440/67331
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Type: | Conference paper |
Title: | Customizable Instance-Driven Webpage Filtering Based on Semi-Supervised Learning |
Author: | Zhu, M. Hu, W. Li, X. Wu, O. |
Citation: | 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 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2007 |
ISBN: | 9780769530260 |
Conference Name: | IEEE/WIC/ACM International Conference on Web Intelligence (2007 : Silicon Valley, California) |
Statement of Responsibility: | Mingliang Zhu, Weiming Hu, Xi Li and Ou Wu |
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. |
Rights: | © 2007 IEEE |
DOI: | 10.1109/WI.2007.26 |
Published version: | http://dx.doi.org/10.1109/wi.2007.26 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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