Design And Implementation Of Jushe Excellent Shopping Mall Based On Personalized Recommendation

With the vigorous development of e-commerce at home and abroad,people have seen the infinite potential of the online consumer market.People draw inspiration from analyzing the development of domestic and foreign e-commerce trading platforms,and then focus on the detailed consumer market.In recent years,the development momentum and potential of the campus market in terms of scale and potential have received great attention.The home improvement platform based on personalized recommendations studied in this thesis aims to provide college students with the preferred high-quality e-commerce platform for campus life.The consumer group is college students,and the commodity category is daily necessities in the dormitory scene.The topic of this paper comes from the actual project that the author participated in the internship company,which is mainly divided into two subsystems: user terminal system and background management subsystem.The user terminal system is developed based on the We Chat mini-program.This system is the core client of the system and provides customers with services such as product display,shopping cart addition,order settlement,and personalized recommendation.The background management terminal system is based on Web development and is mainly used for platform management personnel to manage commodities,orders and user role permissions.With the increasing number of users and products,in order to improve the shopping experience of the majority of users,the system adds personalized recommendation service,which is mainly provided by the personalized recommendation module.From the perspective of recommendation algorithm,personalized module can be divided into: recommendation based on product collection information,collaborative filtering recommendation based on users and collaborative filtering recommendation based on items,which respectively serve different scenarios such as home page recommendation,shopping cart recommendation and order recommendation.In particular,the recommendation based on commodity collection information solves the cold start problem of the system,and provides personalized recommendation services for users.The platform has successfully passed the system test and is put into normal use.The existing user group has reached more than 5,000 people.The maximum daily PV can reach more than a thousand levels.The servers that carry users and commodity data resources are running stably,ensuring platform users Purchase experience.In the process of completing the thesis,I strictly followed the software development process,and completed the requirements analysis,system design,system implementation,and system testing of the thesis alone,and finally realized this system platform.Of users provide a high-quality purchasing service experience.At the same time,the platform is constantly optimizing and iterating.