Emotion-Aware Music Recommendation System: Enhancing User Experience
  Through Real-Time Emotional Context
Tina Babu Rekha R Nair Geetha A
This study addresses the deficiency in conventional music recommendation
systems by focusing on the vital role of emotions in shaping users music
choices. These systems often disregard the emotional context, relying
predominantly on past listening behavior and failing to consider the dynamic
and evolving nature of users emotional preferences. This gap leads to several
limitations. Users may receive recommendations that do not match their current
mood, which diminishes the quality of their music experience. Furthermore,
without accounting for emotions, the systems might overlook undiscovered or
lesser-known songs that have a profound emotional impact on users. To combat
these limitations, this research introduces an AI model that incorporates
emotional context into the song recommendation process. By accurately detecting
users real-time emotions, the model can generate personalized song
recommendations that align with the users emotional state. This approach aims
to enhance the user experience by offering music that resonates with their
current mood, elicits the desired emotions, and creates a more immersive and
meaningful listening experience. By considering emotional context in the song
recommendation process, the proposed model offers an opportunity for a more
personalized and emotionally resonant musical journey.