Adaptive Algorithms For Interface Generation Based On User Behavioral Analytics

Static user interfaces inadequately address the diversity of modern user needs, motivating automated personalization approaches. This study developed and evaluated an algorithmic framework for generating personalized interfaces through multi-dimensional behavioral analytics. Seventeen behavioral metrics across four domains – task performance, navigation behavior, interaction patterns, and engagement indicators – were collected via a custom web-based platform. K-means clustering identified five distinct user profiles, while a decision tree classifier mapped profiles to optimized interface configurations through a three-stage adaptive pipeline. The classifier achieved 84.7% accuracy, and adaptive interfaces reduced task completion time by 18.4%, error rates by 31.7%, and task abandonment by 42.9% relative to static baselines, with SUS scores improving from 68.4 to 81.7. Meaningful adaptation emerged after approximately 47 minutes of interaction. These findings demonstrate that machine learning applied to behavioral analytics can effectively automate interface generation with substantial usability gains, though context-sensitive strategies are needed for behaviorally variable users.