Design And Implementation Of An Online Shopping Platform System With Intelligent Recommendation Function

With the increasing popularity of computer networks and the rapid development of e-commerce,online shopping as a modern fashionable way of shopping has become a part of people’s life.This paper designs and implements an online shopping platform system with intelligent recommendation function.By collecting the data of user interest,user browsing history and user collection,the intelligent recommendation of electronic products with reference value will be made to users,so as to improve user service satisfaction,improve user shopping experience and help sales enterprises sell goods quickly.This paper analyses the current situation of intelligent recommendation of online shopping platform,and makes a thorough study and comparison of the existing online shopping platform.On this basis,an online shopping platform system with intelligent recommendation function based on SSH technology is designed and implemented.The system is divided into three recommendation modes,which are recommendation mode according to user preferences,recommendation mode based on user shopping cart records and recommendation mode based on user favorites records.In the first recommendation mode,user preferences come from user preferences questionnaire,and the system makes recommendation by associating the products within the scope of user preferences.In the latter two recommendation modes,the temporal association rule algorithm and user-based collaborative filtering algorithm are used to mine the user’s preference set in the user’s shopping and collecting behavior,and the attenuation model algorithm is used to mark the preference weight of the user’s behavior.The combination of the two algorithms generates the list of recommended goods.This system can accurately recommend goods by finding out the relationship between users’ preferences and commodities,thus realizing the intelligent demand of customers for online shopping.