Classification of e-commerce web pages using statistical descriptor vectors

Abstract Nowadays, there is a huge volume of information on the web, which is disseminated to the users in a chaotic way. In order to be easily accessed, the information must be clustered and classified in appropriate knowledge areas. Thus, many heavily visited sites or Portals try to unify the access to multiple information sources, providing by this way classification of information. This paper proposes a system, aiming to classify e-commerce web pages and sites according to their web-content. This system can be implemented for automatic knowledge segmentation in a Portal or in a search engine repository. The system performance reached 93% in the first test sets, after the learning phase. However, relevance feedback mechanisms increase the performance significantly (up to 95%) as the number of the test sets increases.