Application Reseach Of Data Mining On E-Commerce Recommendation System

With the development of computer technique and computer network technique by leaps and bounds, electronic commerce, as a kind of new business mode, also got to advance quickly. In current electronic commerce, how to set up a good relation with customers, how to improve customers'loyalty, and how to gain new customers while preserving regular customers, are all very important problems. And it needs electronic commerce enterprise to understand customers'favorites, in order to provide an individual service to customers exactly, recommend the goods that customers are interested in, and help customers find their needs easily and rapidly.Data mining technique is to find and extract potential, interesting and hidden information as well as useful model. Combine data mining with electronic commerce, and build an electronic commerce recommendation system model. It can get valuable knowledge by analyzing the favorites, behavior and content of users'access on web; therefore, it can get the users'usage patterns, and then provide personalized services to users through the analysis and understanding of users'characterization.This paper built an electronic commerce recommendation system model based on data mining according to the accuracy and efficiency, meanwhile did a depth study of electronic commerce recommendation algorithm, and improved the collaborative filtering algorithm by adding the furthest new accumulation improvement project, in order to enhance the efficiency and lighten the data sparseness problem, at last carried on a realization of the recommendation system and gave the experiment research result.