E-commerce Personalized Recommendation Model

With application and the development of Electronic Commerce, integrated researches of artificial intelligence and web mining and commercial model have become a forward problem. Electronic Commerce websites provide more and more options for customers, at the same time, increase the difficulty that customer, who face with plenty of information, find out the product information coincide with their needs accurately and quickly. Personalization recommendation technology analyses customers' related information, and offers products and service to customers which coincide with their preferences actively and efficiently, on the one hand, the customer personalized demand was satisfied much more better on the other hand, it is favorable to establish steady customers crowd, improve service quality, and enhance the enterprise's market competition ability greatly. Therefore the research of the personalized recommendation model has higher academic value and application prospect.This dissertation analyses research accomplishment and actual application environment, studies the personalized recommendation model which was based on customer purchase behaviors and preferences, and filters customer samples with sampling technology in order to improve the accuracy and efficiency of recommendation. Mainly research accomplishment includes:(1) This dissertation adapts dynamic mining customer individual behavior. Because most of traditional technology predict customer preferences based on the static data, but customer preference is changing with time. So this dissertation sorts the purchase of customer behavior as purchase behavior sequence, according to the time order, and then extracts the association rule, and predicts target customer's current or future preferences, which has raised the accuracy of the forecast.(2) This dissertation adapts sampling for pre-processing sample data. This dissertation takes lookahead selective sampling algorithm in use, through defining sample label utility , and chooses the customer sample with the biggest utility for labeling as the recommendation accordance. And it solves sparsity and extensibility of...