The Design And Realization Of Recommendation System Of Electronic Commerce

E-commerce developes rapidly in recent ten years, people’s traditional concept of consumption is also changing all the time, in such an environment, how to meet the needs of the consumers, to allowthem to find their needing goods in vast amounts of information, to enhance consumers’ experience and satisfaction, thus to attract and retain consumers, is particularly important. Recommendation systembased on the analysis ofuser’s commodity or information, produces strongly targeted recommendations for the users, so as to realize the above purpose.This dissertation analyses research accomplishment and actual applicationenvironment, studies the personalized recommendation model which was based oncustomer purchase behaviors and preferences, and filters customer samples withsampling technology in order to improve the accuracy and efficiency ofrecommendation. Research accomplishment mainly includes:1、This dissertation offers a dynamic method to mine customers behaviors. Because most of traditional technology predict customer preferences based on the static data, butcustomer preference is changing with time. So this dissertation sorts the purchase ofcustomer behavior as purchase behavior sequence, according to the time order, and thenextracts the association rule, and predicts target customer’s current or future preferences,which has raised the accuracy of the forecast.2 、 This dissertation offers sampling for pre-processing sample data. This dissertation takes look ahead selective sampling algorithm and collaborative filteringalgorithm based on the combination of project and customer in use. Computer, throughdefining sample label utility, chooses the customer sample with the maximum utility forlabeling as the recommendation basis. And it solves sparsity and extensibility oftraditional collaborative filtering, which provides a way for reducing cost and raisingquality of recommendation.The purpose of this paper is to solve the contradiction between data and user needs in a flood of e-commerce system between the proposed pretreatment + sampling personalized recommendation technology to improve the personalized recommendation accuracy, speed up the personalization and intelligent e-commerce system process. Massive development of e-commerce data mining and personalized recommendation system, there are some positive significance.