Entertainment Recommendation and Rating System Based on Emotions

In today’s digital landscape, the abundance of entertainment content on streaming platforms, music services, and e-book libraries presents challenges and opportunities. Traditional recommendation systems often fail to capture users’ dynamic preferences, including their emotional states. Our project addresses this gap by introducing an Emotion-based rating and recommendation system. By integrating real-time facial expression detection, we aim to understand users’ emotional reactions to content. This system responds to the demand for personalized experiences in digital entertainment. Leveraging computer vision and machine learning, our research provides personalized suggestions based on users’ emotional responses, surpassing surface-level preferences. This initiative transforms entertainment interaction by acknowledging emotions’ role in choices and experiences. It revolutionizes content discovery in the digital age, offering holistic entertainment experiences. Experiment results demonstrate the accuracy of emotion recognition and the effectiveness of personalized recommendations. Positive user feedback indicates high satisfaction and engagement, guiding future enhancements.