Research on human-computer interaction personalized experience algorithm based on user experience theory

In order to study the role of user experience in automotive human-computer interaction product design, this paper first introduces adversarial q network and deep q network content. Adversarial Q-Network (AQN) is an innovative framework that combines the advantages of Generative Adversarial Network (GAN) and Deep Q-Network (DQN) to enhance the personalized automotive user experience based on human-computer interaction. AQN uses the powerful generation capabilities of GAN to create realistic and diverse user behavior data, thereby enhancing the authenticity and diversity of the training set for use by DQN. The GAN segment generates synthetic but authentic user interaction data that is used to train the DQN to make more informed decisions to personalize each driver's in-car experience. This dual network architecture enables AQN to dynamically adapt to individual preferences and optimize all aspects of the driving experience, such as climate control, entertainment systems, and navigation AIDS. Finally, by comparing the model architecture with traditional machine learning, this paper verifies that the AQN model proposed in this paper not only improves the accuracy of personalized recommendation, but also improves the overall user satisfaction and user engagement. The feasibility and effectiveness of this model in the optimization of automobile users' personalized experience are proved.