Design And Implementation Of Intelligent Recommendation Mall System Based On Collaborative Filtering

With the rise of the Internet era and the progress of science and technology,people's lives are also undergoing tremendous changes.Information overload has become an important problem in the current computer applications.Users need to spend time from a large amount of information to extract the information they need.The recommendation system can filter out effective information for users to solve this problem.At present,collaborative filtering recommendation,hybrid recommendation have been applied in many Internet platforms to recommend the desired information for users.Similarly,with the development of e-commerce,online shopping has become an integral part of people's life.This paper designs and implements an intelligent mall recommendation system based on collaborative filtering.One is the user's part,including personal information management,commodity query,shopping cart management,commodity ordering,order management and other functions.The other is the platform module,which includes dynamic allocation of permissions,user management,commodity information maintenance,commodity evaluation management,commodity off shelf,automatic confirmation of receipt and other functions.At the same time,the system can collect user data according to to recommend suitable products for users.The system also uses the technology in the design,adopts the development mode of micro service and separation of front and back end,adopts the spring cloud framework in the overall framework of the system,improves the development efficiency,selects the Vue framework in the front-end technology,makes the system page response more fluent and selects the application ratio in the database My SQL and Redis ensures the performance of the system.In recommendation function module,this paper describes and compares different recommendation methods,and finally selects the partition recommendation method.The user based collaborative filtering algorithm and the item based collaborative filtering algorithm are applied in the intelligent mall system,while the hot item recommendation problem in the traditional collaborative filtering algorithm and the recommendation system are considered The real-time performance and time effect of the system are improved,and the hot items are punished and the time decay function is introduced to optimize.In order to meet the functional requirements of users and the mall platform,this paper studies the related technologies and theories from the aspects of improving users' system satisfaction and the comprehensiveness of the system,and designs and implements an intelligent mall recommendation system based on collaborative filtering.