Words to Waves: Emotion-Adaptive Music Recommendation System

• URL: http://arxiv.org/abs/2510.21724v1
• Date: Wed, 17 Sep 2025 15:35:03 GMT
• Title: Words to Waves: Emotion-Adaptive Music Recommendation System
• Authors: Apoorva Chavali, Reeve Menezes,
• Abstract summary: This paper introduces a novel music recommendation framework employing a variant of Wide and Deep Learning architecture.<n>The framework takes in real-time emotional states inferred directly from natural language as inputs and recommends songs that closely portray the mood.<n> Experimental results show that personalized music selections positively influence the user's emotions and lead to a significant improvement in emotional relevance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current recommendation systems often tend to overlook emotional context and rely on historical listening patterns or static mood tags. This paper introduces a novel music recommendation framework employing a variant of Wide and Deep Learning architecture that takes in real-time emotional states inferred directly from natural language as inputs and recommends songs that closely portray the mood. The system captures emotional contexts from user-provided textual descriptions by using transformer-based embeddings, which were finetuned to predict the emotional dimensions of valence-arousal. The deep component of the architecture utilizes these embeddings to generalize unseen emotional patterns, while the wide component effectively memorizes user-emotion and emotion-genre associations through cross-product features. Experimental results show that personalized music selections positively influence the user's emotions and lead to a significant improvement in emotional relevance.

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