Movie Recommendation System Based on Emotions.

Abstract

"This study aimed to develop a hybrid movie recommendation system by integrating emotion detection and merging collaborative and content-based filtering techniques, significantly enhancing personalization by aligning movie suggestions with users' current emotional states. This innovative approach addresses the cold start problem and data sparsity effectively, utilizing Convolutional Neural Networks (CNNs) for accurate emotion recognition from facial expressions. Evaluation results indicate improved accuracy and user satisfaction, confirming that the integration of emotional alignment and merged filtering techniques increases engagement and relevancy of recommendations Future enhancements could nclude expanding the emotion dataset, refining adaptability across demographics, and integrating contextual factors to further implove recommendation precision. This study contributes to the advancement of recommendation systems and sets the stage for future research in emotion-based user interfaces, highlighting the potential for more empathetic and responsive entertainment technologies. "