01338nas a2200145   4500008004100000245007400041210006900115260002000184300001500204520076300219100002100982700002601003700002401029856013901053       2006                        eng d00aPersonalized News Categorization through Scalable Text Classification0 aPersonalized News Categorization through Scalable Text Classific  c16 - 18 January  a 391 - 4013 aExisting 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.1 aBouras, Christos1 aPoulopoulos, Vassilis1 aAntonellis, Ioannis  uhttp://telematics.upatras.gr/telematics/publications/personalized-news-categorization-through-scalable-text-classification?language=en