Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Characteristic concept representations
Characteristic concept representations
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
One-class svms for document classification
The Journal of Machine Learning Research
Just-in-time information retrieval agents
IBM Systems Journal
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
One-class document classification via Neural Networks
Neurocomputing
Estimating the size and evolution of categorised topics in web directories
Web Intelligence and Agent Systems
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An adaptive system designed to assist in navigating the Web is presented. The core of the system is a user model constructed unobtrusively by observing the user activity and using only positive information to train a certain kind of neural network. The system is built upon neural network techniques designed to attack the problem of user modeling using only positive examples. The system is composed of three main agents: LEARN, CLASSIFY and SHADOW which interact around the neural network model to (respectively) build the user model, apply the user model, and to gather information to train the user model. LEARN has been extensively tested off-WEB on the Reuters data base for information retrieval. CLASSIFY has been used to automatically annotate a WEB-browser with recommendations.