Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A vector space model for automatic indexing
Communications of the ACM
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Clustering Ontology-Based Metadata in the Semantic Web
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Enriching Information Agents' Knowledge by Ontology Comparison: A Case Study
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Assessing semantic similarity among spatial entity classes
Assessing semantic similarity among spatial entity classes
Learning knowledge rich user models from the semantic web
UM'03 Proceedings of the 9th international conference on User modeling
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national 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
Personal assistants: Direct manipulation vs. mixed initiative interfaces
International Journal of Human-Computer Studies
Journal of Network and Computer Applications
A hybrid web recommender system based on Q-learning
Proceedings of the 2008 ACM symposium on Applied computing
Improving Web Search by Categorization, Clustering, and Personalization
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Personalization algorithms for portable personality
Proceedings of the 12th international conference on Entertainment and media in the ubiquitous era
Recommender system based on workflow
Decision Support Systems
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As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages.