Research on the Design and Implementation of an App Recommendation System Based on User Behavior

With the rapid development of mobile internet, personalized recommendation systems have become increasingly important in various applications. This paper designs and implements an APP recommendation system based on user behavior data. First, it elaborates on the basic concepts and core technologies of recommendation systems, analyzes user behavior characteristics and their impact on recommendation accuracy, and proposes algorithms based on collaborative filtering, content-based filtering, and hybrid recommendation. On this basis, the paper constructs the system architecture, develops the recommendation engine, and verifies the effectiveness of the system through performance testing and experimental data. The results show that the APP recommendation system based on user behavior significantly improves recommendation accuracy and enhances the user experience. Finally, the paper evaluates the practical application effects of the system and provides prospects for future research directions.