Design And Implementation Of Personalization Recommender System For Member

With the rapid development and wide application of the network and database technology,E-commerce is growing up and booming up.And the extensive application of e-commerce makes enterprises a large amount of data.As the fierce competition among enterprises,the data, to some extent,become the most valuable resources. Therefore,we want to make use of data mining technology to analyze huge amounts of data and find a potential link between the data from large amounts of useful information to provide intelligent decision support for people and provide consumers individual recommendation services.The recommendation system works like salesman who gives consumers advices and helps them to find what they need. Then the e-commerce system can increase the sales figures, maintaining customer effectively and prevent customers losing.In this paper, we use data mining,and mixed collaborative filtering and rule association technology to design a recommendation system.First of all,this paper discusses the status and development trends of data mining research and recommendation system, designed the personalization recommeder system for e-commerce.The point is the rule association which base on mixed algorithm and improved association algorithm which called Apriori-1.The improved algorithm is based on avoid combined data which is not useful and just search data base for one time.A simulation instance is given in order to show the advantages of this algorithm compared with Apriori, and the new rule association boosted the veracity of the system.