Enhancing User Experience in Human-Computer Interaction Through Advanced Deep Learning Techniques for Personalized and Adaptive Interfaces

In Human-Computer Interaction (HCI), creating a personalized and intuitive user experience is crucial for effective digital interfaces. This study explores how advanced deep learning (DL) algorithms can enhance user interfaces by tailoring them to individual preferences and behaviors. The approach involves a comprehensive methodology beginning with data collection from various sources, capturing user interactions, preferences, and contextual information. Numerical features are then standardized using preprocessing techniques like Min-Max Normalization, and Principal Component Analysis (PCA) is employed for feature selection to identify significant patterns and reduce dimensionality. The study utilizes a Multi-Layer Perceptron-Long Short-Term Memory (MLP-LSTM) hybrid architecture, which integrates MLP and LSTM networks to model complex temporal dependencies and nonlinear relationships within user interaction data. This innovative architecture enables the development of robust models capable of dynamically adapting to user behavior, providing personalized recommendations, and modifying interfaces accordingly. The effectiveness of this approach is validated through substantial improvements in task completion rates, user satisfaction, and engagement. Notably, the proposed method achieved an accuracy of $\mathbf{9 9. 1 2 \%}$, demonstrating its potential to significantly enhance user experience in HCI. Overall, this work showcases the power of cutting-edge deep learning techniques in creating adaptive and user-centered digital interfaces, paving the way for more natural and effective human-computer interactions.