Categorization of product pages depending on information on the Web

Abstract

In this paper, the authors categorize product pages on the Web depending on their information. We used naive Bayes and the complement naive Bayes classifier, and tried four kinds of features to categorize them: all the words of the titles of the product pages, the nouns extracted from the titles, all the words of the titles and the descriptions of the product pages, and the nouns extracted from them. The experiments show that the product pages can be classified most correctly depending on only the nouns of the titles of the product pages. Moreover the complement naive Bayes classifier outperformed the naive Bayes classifier.