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

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dc.contributor.authorZhu, M.
dc.contributor.authorHu, W.
dc.contributor.authorLi, X.
dc.contributor.authorWu, O.
dc.contributor.conferenceIEEE/WIC/ACM International Conference on Web Intelligence (2007 : Silicon Valley, California)
dc.date.issued2007
dc.description.abstractThe 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.
dc.description.statementofresponsibilityMingliang Zhu, Weiming Hu, Xi Li and Ou Wu
dc.identifier.citation2007 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
dc.identifier.doi10.1109/WI.2007.26
dc.identifier.isbn9780769530260
dc.identifier.urihttp://hdl.handle.net/2440/67331
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeOnline
dc.rights© 2007 IEEE
dc.source.urihttp://dx.doi.org/10.1109/wi.2007.26
dc.titleCustomizable Instance-Driven Webpage Filtering Based on Semi-Supervised Learning
dc.typeConference paper
pubs.publication-statusPublished

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