WebMate: A Personal Agent for Browsing and Searching

Liren Chen and Katia Sycara
Conference Paper, Proceedings of the 2nd International Conference on Autonomous Agents and Multi Agent Systems, AGENTS '98, pp. 132 - 139, May, 1998

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The World-Wide Web is developing very fast. Currently, fi nding useful informationon the Web is a time consuming process. In this paper, we present WebMate, an agent that helps users to eff ectively 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 di erent domains. These domains are automatically learned by WebMate. Second, WebMate uses the Trigger Pair Model to automatically extract keywords for refi ning 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 refi nement. Using these techniques, WebMate provides eff ective 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.

author = {Liren Chen and Katia Sycara},
title = {WebMate: A Personal Agent for Browsing and Searching},
booktitle = {Proceedings of the 2nd International Conference on Autonomous Agents and Multi Agent Systems, AGENTS '98},
year = {1998},
month = {May},
pages = {132 - 139},
publisher = {ACM},
keywords = {Information Agents, Instructability, Knowledge acquisition and accumula- tion, long-term adaptation and learning, user modeling},
} 2017-09-13T10:49:32-04:00