Research And Implementation Of Online Shopping System Based On Personalized Recommendation

With the rapid development of Internet and e-commerce technology.Nowadays,online shopping has become an important way of modern shopping.At the same time,the number of people participating in online shopping is increasing year by year.The market is equipped with a very rich variety and quantity of products.But when shuttling in such an environment all day,consumers are often lost in a large number of product information space and can not successfully find the products they need.Therefore,how to find their own needs from a large number of commodities has become the most talked about topic nowadays.Shopping recommendation system is in this context,but at the same time,the existing shopping recommendation system also has some problems,such as the inaccuracy of customer recommendation,low recommendation efficiency and cold start of the system.In this paper,the requirements and functions of the system are analyzed and designed based on the requirements and functions of the system.Then,the recommendation function is optimized and improved by integrating user characteristics and commodity characteristics as well as collaborative filtering recommendation algorithm.Finally,the front-end system and the back-end system are implemented concretely.The design of online shopping system based on personalized recommendation is based on B/S mode.Based on J2 EE platform,the development tool uses Eclipse development platform which is popular based on Java programming language.The JDK version uses 1.8.Maven project management tool is used to build and manage the project.Tomcat 8 is carried on the server.The most popular Spring+Spring MVC+Mybatis framework and JSP interface editing technology are used in the technology.Mysql 5.6 is selected as the background database of the system.The system realizes the basic functions of online shopping and personalized recommendation.At the same time,the improved recommendation algorithm can give the most suitable recommendation according to the customer's personal information,commodity attributes and online shopping behavior characteristics,which improves the accuracy of recommendation and effectively solves the cold start problem of the system.