An emotion-aware music recommender system: bridging the user’s interaction and music recommendation

In emotion-aware music recommender systems, the user’s current emotion is identified and considered in recommending music to him. We have two motivations to extend the existing systems: (1) to the best of our knowledge, the current systems first estimate the user’s emotions and then suggest music based on it. Therefore, the emotion estimation error affects the recommendation accuracy. (2) Studies show that the pattern of users’ interactions with input devices can reflect their emotions. However, these patterns have not been used yet in emotion-aware music recommender systems. In this study, a music recommender system is proposed to suggest music based on users’ keystrokes and mouse clicks patterns. Unlike the previous ones, the proposed system maps these patterns directly to the user’s favorite music, without labeling its current emotion. The results show that even though this system does not use any additional device, it is highly accurate compared to previous methods.