Words to Waves: Emotion-Adaptive Music Recommendation System

TL;DR

This paper presents an emotion-aware music recommendation system that uses real-time emotional inference from natural language to personalize song suggestions, improving emotional relevance and user experience.

Contribution

It introduces a novel framework combining transformer-based emotion inference with Wide and Deep Learning for real-time, emotion-adaptive music recommendation.

Findings

Personalized recommendations improve emotional relevance.

Transformer-based embeddings effectively capture emotional states.

System positively influences user emotions.

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.