Application and improvement of reinforcement learning in intelligent recommender systems

Reinforcement learning, as a machine learning method that can continuously optimize the decision-making strategy by interacting with the environment, has been widely noticed and applied in intelligent recommendation systems in recent years. Compared with traditional recommendation algorithms, reinforcement learning can dynamically capture changes in user behavior and adjust the recommendation strategy in real time, so as to improve the accuracy of personalized recommendation and user experience. In this paper, we first overview the basic application of reinforcement learning in recommender systems, and explain the mechanism of constructing recommendation strategies and improving recommendation effects; then we focus on the analysis of the three key technologies and challenges: user behavior modeling and state representation, reward function design and feedback mechanism, and strategy optimization and exploration strategy; finally, we discuss the development trends of innovative methods based on deep reinforcement learning, multimodal data fusion, and personalized real-time recommendation. Finally, we discuss the innovative methods based on deep reinforcement learning, multimodal data fusion, and personalized real-time recommendation. Through systematic sorting and summarization, this paper aims to provide theoretical guidance and technical reference for the application of reinforcement learning in intelligent recommender systems, and to promote the further development and improvement of this field.