PRESTO: A Recommender of Musical Collaborations Based on Heterogeneous Graph Neural Networks

Fernando Terroso-Saenz; Jesús Soto; Andrés Muñoz; Philippe Roose

doi:10.9781/ijimai.2025.03.004

Authors

Fernando Terroso-Saenz Universidad Politécnica de Cartagena
Jesús Soto Universidad Católica de Murcia
Andrés Muñoz Universidad de Cádiz
Philippe Roose Université de Pau et des Pays de l'Adour

DOI:

https://doi.org/10.9781/ijimai.2025.03.004

Keywords:

Artificial Intelligence Tools, Graph Neural Network, Heterogeneous Graph, Musical Collaborations, Recommender System

Supporting Agencies

Financial support for this research has been provided under grant PID2020-112827GB-I00 funded by MCIN/AEI/10.13039/501100011033. Code Availability The source code of PRESTO is available at https://github.com/ fterroso/the_presto_app

Abstract

The music industry is now more complex and competitive than ever before. In recent years, the search for collaborations with other artists has become a common strategy for musicians to maintain their presence in the sector. Besides, existing music streaming services such as Spotify have exposed large data feeds that can be used to develop innovative services within the realm of music. In this context, the present work introduces PRESTO, a novel recommendation system to suggest musicians for new collaborations with other artists by means of an ensemble of Graph Neural Networks. The system is fed with a heterogeneous graph representing the time evolution and the stationary aspects of a musician’s career. Finally, the proposal has been evaluated with a dataset comprising more than 200,000 artists, with an average F1 score above 0.75.

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