Personalized News Categorization through Scalable Text Classification

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.