When using traditional search engines, users have to formulate queries to describe their information need. This paper discusses a di erent approach t o w eb searching where the input to the search process is not a set of query terms, but instead is the URL of a page, and the output is a set of related web pages. A related web page is one that addresses the same topic as the original page. For example, www.washingtonpost.com is a page related to www.nytimes.com, since both are online newspapers. We describe two algorithms to identify related web pages. These algorithms use only the connectivity information in the web (i.e., the links between pages) and not the content of pages or usage information. We h a ve implemented both algorithms and measured their runtime performance. To e v aluate the e ectiveness of our algorithms, we performed a user study comparing our algorithms with Netscape's \What's Related" service 12]. Our study showed that the precision at 10 for our two algorithms are 73% better and 51% better than that of Netscape, despite the fact that Netscape uses both content and usage pattern information in addition to connectivity information.
In traditional Web search, the input to the search process is a set of query terms, while, in relevant page finding, the input is a given Web page (URL) REF .
Finding related pages in the World Wide Web
IN INTERNATIONAL WORLD WIDE WEB CONFERENCE
Computer Science