Research And Implementation Of A Shopping Recommandation System Based On Data Mining

B2C (B2C, Business to Customer) is that an enterprise provices a new type ofshopping environment for consumer through the Internet, i.e. online store. Customer canshop online and pay online. As the mode can save time and space for both customer andcompany, and improve the efficiency of transaction a lot, especially for busy office worker.In a word, the model can save valueable time from customers.Web data mining is to collect useful information from hyperlink structure of web, webcontent and logs. This paper analyses the shopping behavior of B2C customer, uses severaltechnology of web data mining, designs and realizes a B2C recommendation system. Thefirst part is user management system, and the system has the social network characteristics.Once user logins the system, he can define monitoring target and behavior. Then theinformation collection module will retrieve product related information from Internet, andput into NoSQL DB. Meanwhile information monitoring module will evaluate real-timedata, and it will send notification when data matched what customer expects, that’s therecommendation. Finally, we prove the desighn of the system with practical use and testingof some users.The archivement of significance of the paper is that our recommention system canwork on the Internet with huge data, using several technology of web data mining, andhelp customers to monitor their favorite, so that it can save cost and time for them.