Constructing an Adaptive Optimization Model for Ribbon Recommendation and Interface for User Habits

Aiming at the problem that the interface functional area cannot be dynamically optimized according to the user’s usage habits, this paper proposes an optimization model that integrates user behavior analysis, functional area recommendation and interface adaption. Through front-end behavioral data collection, TF-IDF and PCA extraction of preference features, combined with K-means clustering and graph embedding to model user usage patterns, and constructed a personalized recommendation algorithm integrating collaborative filtering and graph representation. The system significantly improves the task completion efficiency and user satisfaction in actual deployment, and verifies the effectiveness and scalability of the model. The research results provide a feasible path for the optimization of human-computer interaction in complex system interfaces.