A Unified Reinforcement Learning Framework for Dynamic User Profiling and Predictive Recommendation

This paper proposes a unified modeling approach based on reinforcement learning to address the problem of dynamic user profiling and behavior prediction. Profile updating and next-step behavior prediction are formulated as a continuous decision process, where the state is composed of the current profile snapshot and interaction history, the action corresponds to profile updating and recommendation strategy selection, and the reward is driven by user feedback signals. The method models the evolution of user states through a Markov decision process and achieves adaptive iteration of user profiles by applying policy optimization and value function estimation. To ensure balanced modeling, the study integrates a joint objective function of profile updating and behavior prediction within the overall optimization, thereby enhancing long-term stability and personalization. In the experimental design, different methods are systematically compared in terms of accuracy, ranking metrics, and cumulative reward, and the sensitivity of the model under hyperparameter changes, environmental variation, and data disturbance is analyzed. The results show that the proposed method achieves superior performance across multiple evaluation metrics, verifying the effectiveness of the reinforcement learning framework in realizing dynamic profiling and precise prediction in complex interactive environments. This study not only establishes a unified theoretical model but also demonstrates its adaptability and robustness in dynamic settings, providing a systematic solution for user profiling and behavior prediction tasks.