WebMate: A personal agent for browsing and searching

Abstract

The World-Wide Web is developing very fast. Currently, finding useful information on the Web is a time consuming process. In this paper, we present WebMate, an agent that helps users to effectively browse and search the Web. WebMate extends the state of the art in Web-based information retrieval in many ways. First, it uses multiple TF-IDF vectors to keep track of user interests in different domains. These domains are automatically learned by WebMate. Second, WebMate uses the Trigger Pair Model to automatically extract keywords for refining document search. Third, during search, the user can provide multiple pages as similarity/relevance guidance for the search. The system extracts and combines relevant keywords from these relevant pages and uses them for keyword refinement. Using these techniques, WebMate provides effective browsing and searching help and also compiles and sends to users personal newspaper by automatically spiding news sources. We have experimentally evaluated the performance of the system.