Personalized Audiobook Recommendations at Spotify Through Graph Neural
  Networks
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.
65ee820954082af7dbe2e948
Marco De Nadai
admin_assigned
2024-03-11T09:12:05.907Z
626193a918176506fab35c62
/avatars/44a34405e4821fa9047cfa635e198f61.svg
Marco De Nadai
user
marcodena
65ee820954082af7dbe2e949
Francesco Fabbri
admin_assigned
2024-03-11T09:12:12.408Z
64f609c10b49f19f4e6ec978
/avatars/8d01c0a99d6e2bee83c8df2884a8d754.svg
Francesco Fabbri
user
ffabbri
65ee820954082af7dbe2e94a
Paul Gigioli
65ee820954082af7dbe2e94b
Alice Wang
65ee820954082af7dbe2e94c
Ang Li
65ee820954082af7dbe2e94d
Fabrizio Silvestri
admin_assigned
2024-03-11T09:12:49.747Z
6075d282134c000d1ae10d54
/avatars/8691622e1f6325bc378b2268c119b9a0.svg
Fabrizio Silvestri
user
fabrizio-silvestri
65ee820954082af7dbe2e94e
Laura Kim
65ee820954082af7dbe2e94f
Shawn Lin
65ee820954082af7dbe2e950
Vladan Radosavljevic
65ee820954082af7dbe2e951
Sandeep Ghael
65ee820954082af7dbe2e952
David Nyhan
65ee820954082af7dbe2e953
Hugues Bouchard
65ee820954082af7dbe2e954
Mounia Lalmas-Roelleke
65ee820954082af7dbe2e955
Andreas Damianou
Personalized Audiobook Recommendations at Spotify Through Graph Neural
  Networks
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.
A scalable recommendation system integrating heterogeneous graph neural networks and a two-tower model enhances personalized audiobook recommendations by leveraging user preferences and reducing model complexity, resulting in improved streaming rates and benefits to other content types.
Heterogeneous Graph Neural Networks (HGNNs) Two Tower (2T) model multi-link neighbor sampler data sparsity low latency personalized recommendations