Kiana Kheiri, Chen Ding
Emotion-aware recommender systems have received a lot of interest in recent years because of their ability to improve the user experience by adapting recommendations to users’ emotional states. This review article conducts a thorough examination of emotion-aware recommender systems, categorizing them according to the datasets used (video, text, audio, image, and physiological), the application domains (movie, music, social media, and news), the emotion categorization and the methodology used. We investigate multiple issues facing these systems, such as accurately detecting and interpreting emotions, integrating multimodal data, and ethical concerns about user privacy and emotional manipulation. In addition, we explore potential future directions for emotion-aware recommender systems.