<p>User services are tailored by personalized recommender systems according to user interests and preferences. These systems must be dynamic to efficiently acclimate to variations in workerfavorites over time. However, accurately predicting shifts or solving next-item sequences remains challenging. Time-aware recommender systems often leverage users’ rating distributions to predict future preferences, but the scarcity of real-world datasets, characterized by sparse user interactions, regularly reduces accuracy. Unfashionable recommender schemes expression tests such as the long-tail effect, lack of variety, cold-start problems, and data sparsity. Additionally, static data usage fails to arrest the evolving nature of user interests. To statement these matters, this study proposes a dynamic user profiling approach for next-generation personalized recommender systems powered by quantum AI. A modified Bernstein global optimization (MBGO) algorithm is used to address data sparsity through dynamic user grouping. A transfer learning model facilitates user aggregation, resolving cold-start and sparse data challenges. A hybrid quantum–classical recurrent neural network (QCRNN) generates precise user and object representations, ensuring that recommendations align with current user preferences. The suggested model’s performance is validated by using the MovieLens 1&#xa0;M and Amazon Reviews datasets and achieved recommendation accuracy of 98.567%, an 8.5% improvement over state-of-the-art models. The anticipated MBGO + TL + QCRNN model shows enhancements in mitigating sparsity and adapting to dynamic user preferences, providing highly personalized and accurate suggestions.</p>