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Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Published: 13 May 2024 Publication History

Abstract

In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts.

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Cited By

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  • (2025)Hybrid Quality-Based Recommender Systems: A Systematic Literature ReviewJournal of Imaging10.3390/jimaging1101001211:1(12)Online publication date: 7-Jan-2025
  • (2024)Towards Graph Foundation Models for PersonalizationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651980(1798-1802)Online publication date: 13-May-2024
  • (2024)Comparative Evaluation of Word2Vec and Node2Vec for Frequently Bought Together Recommendations in E-Commerce2024 9th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK63289.2024.10773398(1-5)Online publication date: 26-Oct-2024

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cover image ACM Conferences
WWW '24: Companion Proceedings of the ACM Web Conference 2024
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
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Published: 13 May 2024

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Author Tags

  1. audiobooks
  2. graph neural networks
  3. personalization
  4. recommender systems

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WWW '24
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WWW '24: The ACM Web Conference 2024
May 13 - 17, 2024
Singapore, Singapore

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Cited By

View all
  • (2025)Hybrid Quality-Based Recommender Systems: A Systematic Literature ReviewJournal of Imaging10.3390/jimaging1101001211:1(12)Online publication date: 7-Jan-2025
  • (2024)Towards Graph Foundation Models for PersonalizationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651980(1798-1802)Online publication date: 13-May-2024
  • (2024)Comparative Evaluation of Word2Vec and Node2Vec for Frequently Bought Together Recommendations in E-Commerce2024 9th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK63289.2024.10773398(1-5)Online publication date: 26-Oct-2024

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