Research on Personalized Recommendation Model of Brand User Experience Based on Reinforcement Learning

This paper proposes a personalized recommendation model of brand user experience based on reinforcement learning, which aims to improve the interactive experience between brands and users by dynamically optimizing recommendation strategies. The model combines Q-learning and multi-armed bandit algorithm in reinforcement learning to optimize recommendation strategies and explore user needs. Q-learning is used to improve the long-term effect of recommendations and help the system optimize the long-term satisfaction of users; the multi-armed bandit algorithm is used to solve the balance problem between exploration and utilization, and adjust the recommended content in real time to meet the new needs of users. In practice, the proposed strategy can be adjusted dynamically in response to the real time user's feedback, so as to improve the accuracy of the recommendation and the user's satisfaction. In this experiment, the user behavior data of the virtual e-commerce platform is verified. The results indicate that the proposed model can increase the recommendation precision by 15%, increase the user's satisfaction by 10%, and improve the brand loyalty significantly. The study shows that reinforcement learning can be used in personalized recommendation systems, and provides a new way to improve user experience for brands.