Hybrid music recommendation with graph neural networks

Bevec, Matej (author), Tkalčič, Marko (author), Pesek, Matevž (author)

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.

• Language: English
• Keywords: embeddings, music recommendation systems, graph neural networks, beyond-accuracy evaluation, PinSage
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: Str. 1891–1928
• Numbering: Vol. 34, iss. 5
• PID: 20.500.12556/RUL-165162
• UDC: 004.032.26:78
• ISSN on article: 0924-1868
• DOI: 10.1007/s11257-024-09410-4
• COBISS.SI-ID: 206444035
• Publication date in RUL: 25.11.2024

Record is a part of a journal

• Title: User modeling and user-adapted interaction
• Shortened title: User model. user-adapt. interact.
• Publisher: Springer Nature
• ISSN: 0924-1868
• COBISS.SI-ID: 15633925

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: vdelave, glasbeni priporočilni sistemi, nevronske mreže na grafih, vrednotenje nad natančnostjo