Design And Implementation Of Application Ranking System Based On ASO

With the rise of smart phones,there has been a substantial increase in the number and variety of corresponding third-party Applications(Apps).As a result of adapting to the development of contemporary society,the smartphone App has gradually become an indispensable part of people's lives.However,there are many types of Apps on the current market but the categories are ambiguous.Users often see the same Apps under different categories.This makes it difficult for users to quickly and accurately locate related Apps in the App store.Aiming at the situation encountered by current users,the subject of aso-based Application ranking system is proposed.ASO(App store Optimization)is the process of app store optimization,which is to improve App ranking in the app store and search results ranking.Starting from the problems in real life,this project uses the popular Spring Boot architecture,Redis cache technology,MongoDB as no-sql database to finally achieve an App ranking system with both storage optimization and efficient operation properties.First,a demand analysis of the App ranking system was carried out.Among them,the demand functions include program management,task management,task statistics management,ranking record management and other functional modules;at the same time,Non-functional requirements such as the system performance,security,integrity,reliability,ease of use,etc.are introduced.Based on the above requirements analysis,the overall architecture and hierarchical architecture of the Application ranking system are designed.The design of the database conceptual model and physical model of the system,as well as the interface design principles and detailed interface design are introduced.In the system implementation chapter,this article gives the core module code,and uses text to describe the calling process of each function and gives a flowchart.In the test chapter,the test steps of the main functions and the overall test results of the system are introduced.Finally,the work carried out on this subject is summarized,and further optimization directions are proposed.