Emotion-Aware Music Recommendation System: Enhancing User Experience Through Real-Time Emotional Context
Tina Babu, Rekha R Nair, Geetha A

TL;DR
This paper presents an AI-based music recommendation system that detects users' real-time emotions to provide personalized, mood-aligned song suggestions, improving user experience by making music listening more emotionally resonant.
Contribution
It introduces a novel AI model that incorporates real-time emotional detection into music recommendations, addressing limitations of traditional systems that ignore emotional context.
Findings
Enhanced user satisfaction with emotion-aware recommendations
Improved alignment of music with users' current mood
Potential discovery of emotionally impactful lesser-known songs
Abstract
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…
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Taxonomy
TopicsMusic and Audio Processing · Neuroscience and Music Perception · Music Technology and Sound Studies
MethodsALIGN
