Paper Title
Improved Approach to Purchare Prediction on E-Commerce Portal by Analyzing User Behavior Data

While e-commerce has grown quickly in recent years, more and more people are used to utilize this popular channel to purchase products and services on the Internet. Therefore, it becomes very important for shopping sites to predict precisely which items their customers would buy so as to increase sales or improve customer satisfaction. Traditional algorithms such as Collaborative Filtering, has been very popular in predicting users’ preferences in movie, book, or music recommendation areas, but they face the problem that rating data is very sparse or even not available in shopping domain. Compared to the small amount of ratings in e-commerce shopping sites, the quantity of user clicking data is abundant and also contains sufficient information about users’ purchase preferences. Therefore, in this paper we showcase the effectiveness of prediction method based on probability statistics making use of user clicking behavior data. Keywords - E-commerce; Purchase Prediction; Clicking Behavior Data; Probability Statistics.