@article {3085,
	title = {Scalable Text Classification as a tool for Personalization},
	journal = {Computer Systems Science and Engineering, CRL Publishing Ltd},
	year = {2009},
	pages = { 51 - 60},
	abstract = {We consider scalability issues of the text classification problem where by
using (multi)-labeled training documents, we try to build classifiers that assign
documents into classes permitting classification in multiple classes. A new class of
classification problems; called ?scalable?, is introduced, with applications on web
mining. Scalable classification utilizes newly classified instances in order to improve the
accuracy of future classifications and capture changes in semantic representation of
different topics. In addition, definition of different similarity classes is allowed,
resulting in a ?per-user? classification procedure. Such an approach provides a new
methodology for building personalized applications. This is due to the fact that the user
becomes a part of the classification procedure. We explore solutions for the scalable text
classification problem and introduce an algorithm that exploits a new text analysis
technique that decomposes documents into the vector representation of their sentences
according to the user expertise. Finally, a web-based personalized news categorization
system that bases upon this approach is presented.},
	author = {Christos Bouras and Vassilis Poulopoulos and Ioannis Antonellis}
}
