title: Personalized Audiobook Recommendations at Spotify Through Graph Neural
    Networks
summary: 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.
paper_authors._id: 65ee820954082af7dbe2e948
paper_authors.name: Marco De Nadai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-11T09:12:05.907Z
paper_authors._id: 626193a918176506fab35c62
paper_authors.avatarUrl: /avatars/44a34405e4821fa9047cfa635e198f61.svg
paper_authors.fullname: Marco De Nadai
paper_authors.type: user
paper_authors.user: marcodena
paper_authors._id: 65ee820954082af7dbe2e949
paper_authors.name: Francesco Fabbri
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-11T09:12:12.408Z
paper_authors._id: 64f609c10b49f19f4e6ec978
paper_authors.avatarUrl: /avatars/8d01c0a99d6e2bee83c8df2884a8d754.svg
paper_authors.fullname: Francesco Fabbri
paper_authors.type: user
paper_authors.user: ffabbri
paper_authors._id: 65ee820954082af7dbe2e94a
paper_authors.name: Paul Gigioli
paper_authors._id: 65ee820954082af7dbe2e94b
paper_authors.name: Alice Wang
paper_authors._id: 65ee820954082af7dbe2e94c
paper_authors.name: Ang Li
paper_authors._id: 65ee820954082af7dbe2e94d
paper_authors.name: Fabrizio Silvestri
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-11T09:12:49.747Z
paper_authors._id: 6075d282134c000d1ae10d54
paper_authors.avatarUrl: /avatars/8691622e1f6325bc378b2268c119b9a0.svg
paper_authors.fullname: Fabrizio Silvestri
paper_authors.type: user
paper_authors.user: fabrizio-silvestri
paper_authors._id: 65ee820954082af7dbe2e94e
paper_authors.name: Laura Kim
paper_authors._id: 65ee820954082af7dbe2e94f
paper_authors.name: Shawn Lin
paper_authors._id: 65ee820954082af7dbe2e950
paper_authors.name: Vladan Radosavljevic
paper_authors._id: 65ee820954082af7dbe2e951
paper_authors.name: Sandeep Ghael
paper_authors._id: 65ee820954082af7dbe2e952
paper_authors.name: David Nyhan
paper_authors._id: 65ee820954082af7dbe2e953
paper_authors.name: Hugues Bouchard
paper_authors._id: 65ee820954082af7dbe2e954
paper_authors.name: Mounia Lalmas-Roelleke
paper_authors._id: 65ee820954082af7dbe2e955
paper_authors.name: Andreas Damianou
paper_title: Personalized Audiobook Recommendations at Spotify Through Graph Neural
    Networks
paper_summary: 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.
paper_ai_keywords: Heterogeneous Graph Neural Networks (HGNNs) Two Tower (2T) model scalable recommendation system multi-link neighbor sampler low latency personalized recommendations