20.500.12556/RUL-165162 Hybrid music recommendation with graph neural networks Modern music streaming services rely on recommender systems to help users navigate within their large collections. Collaborative filtering (CF) methods, that leverage past user–item interactions, have been most successful, but have various limitations, like performing poorly among sparsely connected items. Conversely, content-based models circumvent the data-sparsity issue by recommending based on item content alone, but have seen limited success. Recently, graph-based machine learning approaches have shown, in other domains, to be able to address the aforementioned issues. Graph neural networks (GNN) in particular promise to learn from both the complex relationships within a user interaction graph, as well as content to generate hybrid recommendations. Here, we propose a music recommender system using a state-of-the-art GNN, PinSage, and evaluate it on a novel Spotify dataset against traditional CF, graph-based CF and content-based methods on a related song prediction task, venturing beyond accuracy in our evaluation. Our experiments show that (i) our approach is among the top performers and stands out as the most well rounded compared to baselines, (ii) graph-based CF methods outperform matrix-based CF approaches, suggesting that user interaction data may be better represented as a graph and (iii) in our evaluation, CF methods do not exhibit a performance drop in the long tail, where the hybrid approach does not offer an advantage. embeddings music recommendation systems graph neural networks beyond-accuracy evaluation PinSage vdelave glasbeni priporočilni sistemi nevronske mreže na grafih vrednotenje nad natančnostjo true false true Angleški jezik Slovenski jezik Članek v reviji 2024-11-25 14:27:54 2024-11-25 14:27:55 2024-11-26 03:54:02 0000-00-00 00:00:00 2024 0 0 Str. 1891–1928 iss. 5 Vol. 34 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.032.26:78 0924-1868 10.1007/s11257-024-09410-4 206444035 s11257-024-09410-4.pdf s11257-024-09410-4.pdf 1 15802CC631EC5B7312C4FC19A8653AFF 97bedd68654ca61b7f52b5efa92e1f36c7bff37af4713851f69c28a5badbb692 2e3f86fd-ab31-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196147 https://link.springer.com/article/10.1007/s11257-024-09410-4 1 ace7b7de-ab30-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196146 Fakulteta za računalništvo in informatiko 0 0 0