Pre-processing of user behaviour for e-commerce

In today's highly used internet considering approximately 205 million online shoppers each day, entrepreneurs are falling into online business. Hence to draw attention to a e-commerce store, Online Advertising is one of the best ways to reach new leads. Recommendation system have attracted nowadays in the field of web application systems and online information retrieval. These systems are applied in various domains such as movies, news, and online e-commerce. For better online e-commerce, personal recommendation is very important. In this paper, we focus on pre-processing of the user behaviour towards products which is the first step for designing a good personal recommendation system. The proposed work which consists of 4 steps: Data Collection, pre-processing, statistical analysis of the user's behaviour to predict user's interest based on their order history and Summarization.