In the realm of intelligent human–machine interaction systems, delivering adaptive and user-specific responses is essential for enhancing operational efficiency and user satisfaction. Traditional systems often lack the ability to dynamically adapt to changing user behaviors, resulting in suboptimal interaction outcomes. This research proposes a novel hyper-personalization framework that integrates meta-learning with deep ensemble-based user classification to enable intelligent and context-aware human–machine interactions. The framework begins with advanced data preprocessing, followed by user behavior segmentation through a hybrid hierarchical K-Means clustering algorithm, capturing granular interaction patterns. A Neural Collaborative Filtering (NCF)-based deep ensemble model is then employed to model complex user-system interactions and improve predictive performance. To achieve hyper-personalization, the system incorporates Model-Agnostic Meta-Learning (MAML), which enables rapid adaptation to new or evolving user behavior with minimal retraining. This adaptive capability supports scalable deployment across diverse machine-interaction scenarios. Experimental results demonstrate a performance accuracy of 98.6%, outperforming baseline models such as CNN, Bi-LSTM, and CNN-LSTM. The proposed framework highlights the potential for next-generation intelligent systems that require scalable, context-aware, and personalized interaction mechanisms.
Kollu et al. (Mon,) studied this question.
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