Design And Implementation Of The Personalized Push Based On Collaborative Filtering

In recent years,personalized push has been widely used in various applications as a strategy to effectively mobilize and recall users.With the widespread use of machine learning algorithms on the Internet,the era of artificial intelligence based on big data has arrived.In recent years,how to apply machine learning models to personalized push scenarios to reduce the harassment of users' invalid push is the focus and difficulty of current research.The thesis first analyzes the functional and non-functional aspects of the system requirements,and on this basis,carries out the overall design of the system functionality;in the overall design part,introduces the target principles of system design,system design architecture,and collaborative filtering The recommended model of the system,the detailed design of the system modules and the design of the system database;finally realized all the functions of the system modules,and tested the functionality and non-functionality of the system from the aspect of system quality.The system screens according to the pushed targets,recalls users who meet the requirements,scores according to the collaborative filtering scoring matrix,and returns the user list corresponding to the items with the highest similarity.Screen the target users in the returned user list for online testing,and decide whether to go online or return to the collaborative filtering model training part according to the relationship between the test score and the basic score.When the task meets the online conditions,the user list submitted by the recommendation model is officially pushed,and the task push progress is tracked,and the effect of the pushed indicators in various dimensions is comprehensively analyzed to achieve auxiliary operational decision-making and The purpose of effect monitoring.When the system is designed and implemented,the standards of high cohesion and low coupling of software design are fully considered,and a high degree of independence from the data source to the functional modules of the system is realized.Modification and adjustment of the modules are unaware of the system platform.,With high scalability.The visualization platform of the result table name system is more conducive to the development,analysis and management of push tasks,facilitates task query and backtracking,and is of great significance to the business expansion of personalized push.