Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914708053819392 |
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| author | De Nadai, Marco Fabbri, Francesco Gigioli, Paul Wang, Alice Li, Ang Silvestri, Fabrizio Kim, Laura Lin, Shawn Radosavljevic, Vladan Ghael, Sandeep Nyhan, David Bouchard, Hugues Lalmas-Roelleke, Mounia Damianou, Andreas |
| author_facet | De Nadai, Marco Fabbri, Francesco Gigioli, Paul Wang, Alice Li, Ang Silvestri, Fabrizio Kim, Laura Lin, Shawn Radosavljevic, Vladan Ghael, Sandeep Nyhan, David Bouchard, Hugues Lalmas-Roelleke, Mounia Damianou, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05185 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks De Nadai, Marco Fabbri, Francesco Gigioli, Paul Wang, Alice Li, Ang Silvestri, Fabrizio Kim, Laura Lin, Shawn Radosavljevic, Vladan Ghael, Sandeep Nyhan, David Bouchard, Hugues Lalmas-Roelleke, Mounia Damianou, Andreas Information Retrieval Machine Learning 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. |
| title | Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2403.05185 |