Affective Recommender Systems

Affective Recommender Systems are technology tools that personalize suggestions (like movies, music, or products) by recognizing and understanding users' emotions and mood states. They analyze cues such as facial expressions, voice tone, or interactions to interpret how a person feels, then tailor recommendations to match or influence their emotional experience. This approach aims to create more engaging and satisfying user interactions, making recommendations feel more relevant and emotionally resonant, ultimately enhancing the overall user experience through emotionally intelligent personalization.