Adaptive Interfaces for Personalized User Experience: A Machine Learning Approach

Abstract

Background: Traditional static interfaces are difficult to meet the diverse needs of different user groups.Methods: A multi-objective scenario Bandit model is used to model interface configuration selection, and meta-learning technology is used to alleviate the cold start phenomenon. A double-insurance counterfactual evaluation technology is used to ensure system launch, and the latency budget target is achieved with the help of drift monitoring and security gating technologies.Results: During the log replay and traffic grayscale testing phases, compared with the strongest baseline and the most advanced technology, the comprehensive performance indicators increased by 0.9% to 5.2% and 3.7% to 5.1%, respectively. The p-value of the vast majority of slices did not exceed 0.01. The error calibration result was below 0.04. In the scenario of one million users participating and 100 actions being executed, the latency value of 95% of users was between 210 and 240 milliseconds. After the cross-domain migration, the cSAT fluctuation value did not exceed 0.07 and recovered quickly.Conclusion: Our engineered pipeline, integrating multi-objective learning, meta-learning, and security assessment/gating techniques, achieves stable and repeatable personalization gains within a time limit and is suitable for large-scale adaptive interface implementations.