A cutting-edge artificial intelligence paradigm for entertainment-infused music recommendations

Abstract

Personalized and fascinating music recommendations are becoming increasingly in demand as the advent of technology continues to change the way that people consume music. Traditional music recommendation systems primarily rely on user listening history and preferences, often neglecting the emotional and experiential dimensions that make music a deeply personal and entertaining endeavour. This research introduces a music recommendation system after classifying the tracks using a novel Gravitational Search Optimized Recursive Neural Networks (GS-RNN) approach. GS-RNN addressed this gap by integrating the Gravitational Search Algorithm (GSA) with Recursive Neural Networks (RNN) to create a content-based recommendation system that assesses audio signal similarity. Evaluation metrics, including accuracy (80%), logarithmic loss (0.85), precision (84%), recall (83%), and F1-score (88%), demonstrate GS-RNN’s superiority over existing techniques. Genre-specific accuracy analysis further underscores the model’s capability to suggest songs within the same genre. Overall, GS-RNN presents a novel and effective paradigm for music recommendations.