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ML92 Proceedings of the ninth international workshop on Machine learning
Artificial intelligence: a modern approach
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
On the merits of building categorization systems by supervised clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the ninth international conference on Information and knowledge management
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Improving hierarchical text classification using unlabeled data
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning of probabilistic concept hierarchies
Machine Learning and Its Applications
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW
Autonomous Agents and Multi-Agent Systems
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Feature selection on hierarchy of web documents
Decision Support Systems - Web retrieval and mining
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Incremental Approach to Building a Cluster Hierarchy
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
User Profiling for Web Page Filtering
IEEE Internet Computing
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An information-theoretic external cluster-validity measure
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Artificial intelligence
Enabling topic-level trust for collaborative information sharing
Personal and Ubiquitous Computing
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Information agents have emerged in the last decade as an alternative to assist users to cope with the increasing volume of information available on the Web. In order to provide personalized assistance, these agents rely on having some knowledge about users contained into user profiles, i.e., models of users preferences and interests gathered by observation of user behavior. User profiles have to summarize categories corresponding not only to diverse user information interests but also to different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, the discovery of interest categories using document clustering offers the advantage that an a priori knowledge of user interests is not needed, therefore the process of acquiring profiles is completely unsupervised. However, most document clustering algorithms are not applicable to the problem of incrementally acquiring and modeling interests because of either the kind of solutions they provide, which do not resemble user interests, or the way they build such solutions, which is generally not incremental. In this paper we describe and evaluate a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), designed to support learning of user interests by personal information agents. WebDCC algorithm carries out incremental, unsupervised concept learning over Web documents with the goal of building and maintaining both accurate and comprehensible user profiles. Empirical evaluation of using this algorithm for user profiling and its advantages with respect to other clustering algorithms are presented.