Applying Web analysis in Web page filtering

Author

Chau, Michael

Author_Institution

Fac. of Bus. & Econ., Hong Kong Univ., China

fYear

2004

fDate

7-11 June 2004

Firstpage

376

Abstract

Vertical search engines provide Web users with an alternative way to search for information on the Web by providing customized searching in particular domains. However, two issues need to be addressed when developing these search engines: how to locate relevant documents on the Web and how to filter out irrelevant documents from a set of documents collected from the Web. This paper reports the research in addressing the second issue. In this research a machine learning-based approach that combines Web content analysis and Web structure analysis is proposed.

Keywords

Internet; Web sites; information filters; information retrieval; learning (artificial intelligence); neural nets; online front-ends; pattern classification; support vector machines; Web content analysis; Web document searching; Web page classification; Web page filtering; Web structure analysis; information retrieval; machine learning; neural network; support vector machine; vertical search engine; Information filtering; Information filters; Information retrieval; Machine learning; Neural networks; Search engines; Support vector machine classification; Support vector machines; Text categorization; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on

Print_ISBN

1-58113-832-6

Type

conf

DOI

10.1109/JCDL.2004.1336155

Filename

1336155