Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Distributed Hypertext Resource Discovery Through Examples
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Two-level Web agent for limited accessibility
COMPSAC '97 Proceedings of the 21st International Computer Software and Applications Conference
User-Centered Agents for Structured Information Location
E-Commerce Agents, Marketplace Solutions, Security Issues, and Supply and Demand
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
VIPAS: virtual link powered authority search in the web
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Collaborative information filtering by using categorized bookmarks on the web
INAP'01 Proceedings of the Applications of prolog 14th international conference on Web knowledge management and decision support
Machine learning of user profiles: representational issues
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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We describe a software agent that learns to find information on the World Wide Web (WWW), deciding what new pages might interest a user. The agent maintains a separate hotlist (for links that were interesting) and coldlist (for links that were not interesting) for each topic. By analyzing the information immediately accessible from each link, the agent learns the types of information the user is interested in. This can be used to inform the user when a new interesting page becomes available or to order the user's exploration of unseen existing links so that the more promising ones are investigated first. We compare four different learning algorithms on this task. We describe an experiment in which a simple Bayesian classifier acquires a user profile that agrees with a user's judgment over 90% of the time.