User Experience Personalized Design Model Based on Artificial Intelligence

In response to the difficulty of accurately identifying user needs and adjusting interfaces in real-time in personalized user experience design, this study introduces an artificial intelligence design model based on K-means algorithm, aiming to enhance the personalization and interactivity of user interfaces. Firstly, the K-means algorithm is used to analyze online activity logs such as browsing time, click through rate, and preferences of users, in order to form a clear user preference group. Next, the decision tree algorithm is used to analyze the clustering results of user behavior, automatically inferring the most suitable interface elements and interaction modes to optimize the layout and functional configuration of the user interface. Finally, A/B testing is implemented in user scenarios, and eye tracking technology and user satisfaction surveys are used to evaluate user responses to two different designs, analyzing and integrating relevant successful cases from the International Conference of Experience Design. The experimental results show that in the smart home interface design experiment, the use of this model significantly improves the efficiency of interface operation. In the application of e-commerce platforms, in traditional design (A), the click through rate ranges from 0.076 to 0.478; in personalized design (B), the click through rate ranges from 0.203 to 0.81, and the purchase conversion rate is also improved, proving the effectiveness of the model in enhancing user experience personalization. The AI driven design model developed in this study effectively enhances the personalization of user experience, demonstrating its practicality and feasibility in multiple practical application scenarios.