Online user Behavior Analysis On Graphical Model
Online shopping is growing on large scale. People purchase their products via internet. They just have to choose their products and make the payment. Users get their products on doorstep. Online shopping had made people’s life easier and faster. As online shopping is increasing, large amount of data on people’s online activities have become available on web. Use of such data can benefit a lot of applications. User behavior, online customer classification can be extracted from these web data. We proposed a system where we can extract the user’s online shopping behavior. System will extract user’s online behavior pattern and will show in graphical format. This graphical format helps the admin during decision making process. We propose a graphical hidden state model based on statistical features and integrate all available information sources to simulate the decision making process. The proposed system, lead to nearly 30% of improvement on million click datasets. This system will be online web application where many products will be displayed on web page. User can view and purchase the products. User sequential behavior pattern is tracked by the system and is put in graphical format which helps during decision making process. This system helps the admin to know most frequently purchased products by the customer. Admin will also get to know which products are in demand. So he can make the decision based on the online behavior pattern of the customer. As user behavior pattern is put up in graphical format it will be easier for the admin to view the data and can make decision process faster and can come up with solution quicker.