The Web is full of information and resources. People have at least three ways to find information they need: (1) by browsing (following hyper-links that seem of interest to them), (2) by sending a query to a search engine, such as Altavista, (3) by following existing categories in search engines, such as Yahoo or Lycos. The problem is that people have to spend a lot of time and effort to navigate but may not find interesting personalized information. However, it is difficult to find the wanted information because a user can't accurately express what he wants and search engines don't adapt their search strategies according to different users. Moreover, the problem is exacerbated because the information sources have high ``noise'', i.e. most of the pages are irrelevant to a particular user's interests. We have developed 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.

For more information, visit the Web Mate Project Page.

Multidisciplinary University Research Initiative (MURI)
Principal Investigator: Katia Sycara
Sponsored by: Office of Naval Research (ONR)
ONR Contact: Michael Shneier
© 1998 Carnegie Mellon Robotics Institute