The Research Of Customer’s Online Shopping Behavior Analysis And Prediction System

With rapid growth of the scale of e-commerce transactions, it has produced a huge amount of customer purchasing behavior data. It is a research hotspot that how to predict the customer’s online shopping behavior by using customer purchasing behavior knowledge which is mined from the customer purchasing behavior data by using data mining, machine learning and other tools. Grasping the customer purchase behavior law can help company to identify and locate the potential customers groups in e-commerce, improve the website s traffic, change the visitors into buyers, control cost effectively, put forward appropriate business strategy and optimize storage. So that it has strong practical significance and economic value.This paper puts forward a system of the customer’s online shopping behavior analys is and prediction after analyzing and studying the def iciency and challenges of traditional method of customer’s online shopping behavior prediction. The System can obtain the potential knowledge of customer’s purchase behavior law by analyzing the customer purchasing behavior data, and then the obtained knowledge will be stored in the knowledge base. According to the customer’s real-time browsing behavior and the customer’s personal attributes, the system realizes the real-time tendency prediction of the customer purchasing behavior based on the knowledge in the knowledge base The main research work is as follows:1) Knowledge discovery. Discover the knowledge which can reflect the electronic commerce customer purchase behavior from customer purchasing behavior by using data mining, machine learning, statistics and other methods, and then extract these laws from the data to support the prediction of customer purchasing behavior.2) Knowledge storage and representation. This paper takes graph as the knowledge representation of system, and constructs a graph of customer behavior knowledge. Uses RDF to express the data model of knowledge graph, and implements the storage for knowledge graph of customer behavior based on HBase.3) Predict Customer’s online shopping behavior. The paper selects the dimension that w hich products the customers want to buy as the target of prediction. This system can acquire the products sequence Based on the relationship knowledge between the products, and then implement the tendency prediction of customer purchase behavior based on c ustomers’ attributes by using TOPSIS method.