Words to Waves: Emotion-Adaptive Music Recommendation System
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.
Cite
@article{arxiv.2510.21724,
title = {Words to Waves: Emotion-Adaptive Music Recommendation System},
author = {Apoorva Chavali and Reeve Menezes},
journal= {arXiv preprint arXiv:2510.21724},
year = {2025}
}