Personalized news categorization through scalable text classification

Authors:
Ioannis Antonellis;Christos Bouras;Vassilis Poulopoulos
Affiliations:
Research Academic Computer Technology Institute N. Kazantzaki, Patras, Greece;Research Academic Computer Technology Institute N. Kazantzaki, Patras, Greece;Research Academic Computer Technology Institute N. Kazantzaki, Patras, Greece
Venue:
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Year:
2006

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Abstract

Existing news portals on the WWW aim to provide users with numerous articles that are categorized into specific topics. Such a categorization procedure improves presentation of the information to the end-user. We further improve usability of these systems by presenting the architecture of a personalized news classification system that exploits user’s awareness of a topic in order to classify the articles in a ‘per-user’ manner. The system’s classification procedure bases upon a new text analysis and classification technique that represents documents using the vector space representation of their sentences. Traditional ‘term-to-documents’ matrix is replaced by a ‘term-to-sentences’ matrix that permits capturing more topic concepts of every document.