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

• Fernando Terroso-Saenz ^[1] ; Jesús Soto ^[2] ; Andrés Muñoz ^[3] ; Philippe Roose ^[4]
  1. [1] Universidad Politécnica de Cartagena

     Universidad Politécnica de Cartagena

     Cartagena, España

     
  2. [2] Universidad Católica San Antonio

     Universidad Católica San Antonio

     Murcia, España

     
  3. [3] Universidad de Cádiz

     Universidad de Cádiz

     Cádiz, España

     
  4. [4] University of Pau and Pays de l'Adour

     University of Pau and Pays de l'Adour

     Arrondissement de Pau, Francia

     

  Mostrar afiliaciones +
• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 9, Nº. 6, 2026, págs. 28-37
• Idioma: inglés
• DOI: 10.9781/ijimai.2025.03.004
• Enlaces
  • Texto completo
• Resumen

  • 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.

• Referencias bibliográficas
  • [1] P. Williamson, J. Luebbers, “Expanding models of music composition: Exploring the value of collaboration,” International Journal of Music...
  • [2] International Federation of the Phonographic Industry (IFPI), “Global music report 2023: State of the industry,” 2023. [Online]. Available:...
  • [3] J. Parc, Y. Kim, “Analyzing the reasons for the global popularity of BTS: A new approach from a business perspective,” Journal of International...
  • [4] R. D. Smith, “The network of collaboration among rappers and its community structure,” Journal of Statistical Mechanics: Theory and Experiment,...
  • [5] Spotify, “The crossover effect: Artist collaborations thrive on Spotify,” 2023. [Online]. Available: https://newsroom.spotify.com/2023-05-24/crossovercollaborations-genre-collabs-streaming-data-spotify/,...
  • [6] C. McDuffie, “Comprender las estadísticas de los fans: contribuciones de los ejecutivos de luminate,” 2022. [Online]. Available: https://artists.spotify.com/es-419/blog/understanding-fan-analytics-luminate-execsweigh-in,...
  • [7] M. O. Silva, G. P. Oliveira, D. B. Seufitelli, M. M. Moro, “Temporal success analyses in music collaboration networks: Brazilian and global...
  • [8] J. McKenzie, P. Crosby, L. J. Lenten, “It takes two, baby! feature artist collaborations and streaming demand for music,” Journal of Cultural...
  • [9] Q. Dai, X.-M. Wu, L. Fan, Q. Li, H. Liu, X. Zhang, D. Wang, G. Lin, K. Yang, “Personalized knowledge- aware recommendation with collaborative...
  • [10] D. Holtz, B. Carterette, P. Chandar, Z. Nazari, H. Cramer, S. Aral, “The Engagement-Diversity Connection: Evidence from a Field Experiment...
  • [11] I. Anderson, S. Gil, C. Gibson, S. Wolf, W. Shapiro, O. Semerci, D. M. Greenberg, ““Just the Way You Are”: Linking Music Listening on...
  • [12] M. O. Silva, L. M. Rocha, M. M. Moro, “Collaboration profiles and their impact on musical success,” in Proceedings of the 34th ACM/SIGAPP...
  • [13] F. Calefato, G. Iaffaldano, L. Trisolini, F. Lanubile, “An in-depth analysis of occasional and recurring collaborations in online music...
  • [14] A. H. Raza, K. Nanath, “Predicting a hit song with machine learning: Is there an apriori secret formula?,” in 2020 International Conference...
  • [15] D. Paul, S. Kundu, “A survey of music recommendation systems with a proposed music recommendation system,” in Emerging Technology in...
  • [16] G. Björklund, M. Bohlin, E. Olander, J. Jansson, C. E. Walter, M. Au-Yong-Oliveira, “An exploratory study on the Spotify recommender...
  • [17] A. Werner, “Organizing music, organizing gender: algorithmic culture and spotify recommendations,” Popular Communication, vol. 18, no....
  • [18] M. Schedl, “Deep learning in music recommendation systems,” Frontiers in Applied Mathematics and Statistics, vol. 5:44, 2019.
  • [19] V. La Gatta, V. Moscato, M. Pennone, M. Postiglione, G. Sperlí, “Music recommendation via hypergraph embedding,” IEEE Transactions on...
  • [20] H. Weng, J. Chen, D. Wang, X. Zhang, D. Yu, “Graph-based attentive sequential model with metadata for music recommendation,” IEEE Access,...
  • [21] H.-G. Kim, G. Y. Kim, J. Y. Kim, “Music recommendation system using human activity recognition from accelerometer data,” vol. 65, p....
  • [22] F. Fessahaye, L. Perez, T. Zhan, R. Zhang, C. Fossier, R. Markarian, C. Chiu, J. Zhan, L. Gewali, P. Oh, “T- recsys: A novel music recommendation
  • system using deep learning,” in 2019 IEEE international conference on consumer electronics (ICCE), 2019, pp. 1–6, IEEE.
  • [23] J. P. Singh, J. K. Bhatia, “Scientific exploration of music recommendation system using facial expressions,” in Advances in Data and...
  • [24] X. Wen, “Using deep learning approach and iot architecture to build the intelligent music recommendation system,” Soft Computing, vol....
  • [25] T. Li, M. Choi, K. Fu, L. Lin, “Music sequence prediction with mixture hidden markov models,” in 2019 IEEE International Conference on...
  • [26] F. Pachet, “Hit song science,” in Music data mining, T. Li and M. Ogihara and G. Tzanetakis Eds., 2011, pp. 305–326, Chapman & Hall/CRC...
  • [27] R. Rajyashree, A. Anand, Y. Soni, H. Mahajan, “Predicting Hit Music using MIDI features and Machine Learning,” in 2018 3rd International...
  • [28] A. H. Raza, K. Nanath, “Predicting a Hit Song with Machine Learning: Is there an apriori secret formula?,” in 2020 International Conference...
  • [29] E. Zangerle, M. Vötter, R. Huber, Y.-H. Yang, “Hit song prediction: Leveraging low-and high-level audio features.,” in Proceedings of...
  • [30] M. Mayerl, M. Vötter, G. Specht, E. Zangerle, “Pairwise learning to rank for hit song prediction,” in Proceedings of BTW 2023, Dresden,...
  • [31] M. Schedl, S. Brandl, O. Lesota, E. Parada-Cabaleiro, D. Penz, N. Rekabsaz, “Lfm-2b: A dataset of enriched music listening events for...
  • [32] M. d. O. Santos-Silva, Collaboration-aware hit song analysis and prediction. PhD dissertation, Universidade Federal de Minas Gerais,...
  • [33] C. E. Shannon, “A mathematical theory of communication,” The Bell system technical journal, vol. 27, no. 3, pp. 379–423, 1948.
  • [34] Y.-W. Cheung, K. S. Lai, “Lag order and critical values of the augmented dickey–fuller test,” Journal of Business & Economic Statistics,...
  • [35] Z. Yuan, X. Zhou, T. Yang, “Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal...
  • [36] W. Hamilton, Z. Ying, J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems,...
  • [37] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, et al., “Graph attention networks,” stat, vol. 1050, no. 20, pp....
  • [38] M. Fey, J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs...
  • [39] B. Rozemberczki, P. Scherer, Y. He, G. Panagopoulos, A. Riedel, M. Astefanoaei, O. Kiss, F. Beres, G. Lopez, N. Collignon, R. Sarkar,...