Facial Emotion-Based Multi-Content Recommendation System: Music, Movies, and Books Tailored to Emotions

Abstract: The value of user experience is greatly increased by personalized recommendations of content in the digital era. The conventional recommender systems are based on tastes or a long history of past users that can never be updated to reflect the current emotional desires in real-time. This study introduces an innovative system, which takes into consideration facial emotion recognition to provide dynamic and emotion-based recommendation related to the three categories of content, music, movies and books. This system allows providing contents that are harmonious with the emotional contexts of users by recognizing happiness, sadness, anger, and neutrality with the help of computer vision and deep learning.

Keywords: Facial Emotion Recognition (FER), Multi-Content Recommendation System,Deep Learning, Convolutional Neural Network (CNN), FER-2013 Dataset, Real-Time Emotion Detection, Content Personalization, Computer Vision, Adaptive User Experience.