Zhu, M.Hu, W.Li, X.Wu, O.2011-11-142011-11-1420072007 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. 663-6669780769530260http://hdl.handle.net/2440/67331The 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.en© 2007 IEEEConference paper002011270610.1109/WI.2007.2627753