A DEEP LEARNING-BASED SYSTEM FOR RECOMMENDING MOVIES BASED ON EMOTIONS

Abstract

In the era of intelligent entertainment, conventional movie recommendation systems often fail to capture the dynamic emotional preferences of users. This paper proposes a novel emotion-based movie recommendation system that utilizes facial expression recognition to detect a user's real-time emotional state and generate personalized movie suggestions accordingly. Using convolutional neural networks (CNNs) for facial emotion classification, the system maps detected emotions to appropriate movie genres through a predefined emotion-genre mapping model. The proposed system enhances user satisfaction by adapting to momentary emotional contexts, unlike static, history-based methods. Experimental results demonstrate that the emotion-aware approach significantly improves recommendation relevance and user engagement. Future extensions may include multimodal emotion detection and integration with user profiles for deeper personalization.