GATSY: graph attention network for music artist similarity
Di Francesco A. G., Giampietro G., Spinelli I., Comminiello D.
Graph Attention Information Retrieval (cs.IR) FOS: Computer and information sciences Recommendation Systems Graph attention Recommendation systems Graph neural networks Information Retrieval Artist similarity Artist Similarity Graph Neural Networks
The artist similarity quest has become a crucial subject in social and scientific contexts, driven by the desire to enhance music discovery according to user preferences. Modern research solutions facilitate music discovery according to user tastes. However, defining similarity among artists remains challenging due to its inherently subjective nature, which can impact recommendation accuracy. This paper introduces GATSY, a novel recommendation system built upon graph attention networks and driven by a clusterized embedding of artists. The proposed framework leverages the graph topology of the input data to achieve outstanding performance results without relying heavily on hand-crafted features. This flexibility allows us to include fictitious artists within a music dataset, facilitating connections between previously unlinked artists and enabling diverse recommendations from various and heterogeneous sources. Experimental results prove the effectiveness of the proposed method with respect to state-of-the-art solutions while maintaining flexibility. The code to reproduce these experiments is available at https://github.com/difra100/GATSY-Music_Artist_Similarity.
Source: PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, pp. 1-8. Roma, Italy, 2025
Publisher: Institute of Electrical and Electronics Engineers Inc.
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@inproceedings{oai:iris.cnr.it:20.500.14243/563682,
title = {GATSY: graph attention network for music artist similarity},
author = {Di Francesco A. G. and Giampietro G. and Spinelli I. and Comminiello D.},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
doi = {10.1109/ijcnn64981.2025.11228629 and 10.48550/arxiv.2311.00635},
booktitle = {PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, pp. 1-8. Roma, Italy, 2025},
year = {2025}
}
