Research on the Application of Collaborative Filtering Based Algorithm in APP Personalised User Experience Design

With the rapid development of artificial intelligence technology, the user’s personalized demand for APP experience is becoming more and more prominent, and personalized user experience design has become the core direction of mobile application (APP) to enhance user stickiness and market competitiveness.Collaborative filtering algorithms, as the core technology of personalized recommendation systems, have significant value in enhancing APP user experience. However, existing research lacks systematic solutions in dealing with problems such as sparse matrix and cold start.To address this challenge, this study constructs an item-user inverse lookup table and innovatively proposes to combine user attribute similarity with user experience similarity based on collaborative filtering algorithms, which significantly outperforms traditional methods in terms of accuracy and coverage. This study not only provides a scalable theoretical framework for APP personalized user experience design, but also has universal reference value for recommendation system optimization in e-commerce, social, education and other fields.