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 personal...

Full description

Saved in:
Bibliographic Details
Published inFrontiers of WWW Research and Development - APWeb 2006 pp. 391 - 401
Main Authors Antonellis, Ioannis, Bouras, Christos, Poulopoulos, Vassilis
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
Category Vector
News Portal
Similarity Class
Similarity Threshold
Topic Concept
Online AccessGet full text
ISBN3540311424
9783540311423
ISSN0302-9743
1611-3349
DOI10.1007/11610113_35

Cover

More Information
Summary: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.
ISBN:3540311424
9783540311423
ISSN:0302-9743
1611-3349
DOI:10.1007/11610113_35