E-commerce web page classification based on automatic content extraction

Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each product are classified into its category by human. It is very hard to define their categories in automatic manner when the data is very large. In this paper, we propose the method for classifying E-commerce web pages based on their product types. Firstly, we apply the proposed automatic content extraction to extract the contents of E-commerce web pages. Then, we apply the automatic key word extraction to select words from these extracted contents for generating the feature vectors that represent the E-commerce web pages. Finally, we apply the machine learning technique for classifying the E-commerce web pages based on their product types. The experimental results signify that our proposed method can classify the E-commerce web pages in automatic fashion.