Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning semantic user profiles from text
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Exploring digital collections to find information relevant to a user's interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users' interests are maintained. This paper presents a new method, based on the classical Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalogues of e-commerce Web sites. Experiments have been carried out on a dataset of real users, and results have been compared with those obtained using an Inductive Logic Programming (ILP) approach and a probabilistic one.