MemoMusic: A Personalized Music Recommendation Framework Based on Emotion and Memory

Abstract

Music is universally recognized as an effective way for human to express emotion and regulate emotional states. But perceived music emotion is subjective and much dependent on culture, environment, and life experience. Therefore, personalized music recommendation is necessary to gain user satisfaction and navigate a listener to a more positive emotional state as well. Existing work on emotion- based music recommendation and personalized music recommendation often lack of considering the impact of past life experiences on music emotion perceiving. We argue that memories associated with music could play a vital role in determining the new emotional states after music listening. To verify our hypothesis, we propose a personalized music recommendation framework called MemoMusic, which estimates the new emotional state of a listener based on an individual’s current emotional state and possible memory associated with the music being listened to. For the preliminary experiment, a dataset of 60 piano music was collected and labelled using the Valence-Arousal model from three categories of Classical, Popular, and Yanni music. Experimental results demonstrate that memory is actually an important factor in determining perceived music emotion. And MemoMusic based on emotion and memory achieves a good performance in terms of improving a listener’s emotional states.