Design And Implementation Of A Personalized E-commerce System Based On Microservice

With the development of network and technology,the pace of people's life has gradually accelerated,and online shopping has become a living habit of most people,therefore various e-commerce platforms have also flourished.While bringing great convenience to consumers,online shopping also brings huge profits to the sellers by saving the cost of the lease rent.At the same time,people have stepped into the era of information overload from the era of information scarcity.Therefore,in order to better meet the needs of users,an excellent shopping system should also support personalized recommendation function to initiatively recommend goods to corresponding users.This personalized e-commerce system is developed under this background.The system adopts the micro service architecture,and is divided into multiple micro services,such as commodity management,user management,commodity search,commodity detail page,shopping cart management,order management and commodity recommendation.Each microservice is quickly built by the Spring Boot framework,which improves the development efficiency.CAS framework is also used to solve the common single sign on problems under the microservice architecture.As for interactions between microservices,Zookeeper is used as the registration and discovery of services,and the middleware Dubbo is adopted to complete the call between services,and the message middleware ActiveMQ is flexibly used to decouple the services.The commodity search module adopts Apache Solr,the search application server,to realize the advanced search function of commodities.The product recommendation module,including offline recommendation based on LFM and Item-CF and real-time recommendation based on custom model,is mainly realized through the Spark framework.The custom model relies on the user's recent scoring records to achieve small calculations and fast response times.The development of the system adopts the separation of front and back ends.The front end mainly uses the AngularJS framework,and the back end mainly uses the SSM framework.The system database uses MySQL and MongoDB,and uses Redis as the cache.As is proved by testing,the independent deployment of microservices effectively improves the system concurrency and ensures the system scalability.The use of Apache Solr,a search application server,realizes advanced search functions for products and improves the user experience.And the cluster mode solves the single machine failure problem of the system and improves the fault tolerance of the system.The use of template engine FreeMarker reduces the response time of the product details page by about one-third,ensuring the real-time nature of the system.The addition of the product recommendation feature has attracted more users and increased product sales and system revenue.