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This AI Research Discusses Personalized Audiobook Recommendations at Spotify Using Graph Neural Networks and Introduces a New Recommendation Engine Called 2T-HGNN

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Spotify, which is well-known for its vast collection of music and talk shows, has expanded its services to include audiobooks to serve a wider range of users. However, this extension comes with certain limitations, especially with regard to customized recommendations. Since audiobooks were originally sold for a price and cannot just be browsed before being purchased, precise and pertinent suggestions are much more important than they are for music and podcasts. 

The challenge of handling sparse data is also present when incorporating a new content type into an already-existing platform. Moreover, due to the enormous volume of content recommendations to millions of individuals, a system that can respond quickly and expand efficiently is required.

In order to address this, a team of researchers has focussed on users’ current musical and podcast interests and and has presented a new recommendation engine known as 2T-HGNN. Using a Two Tower (2T) architecture and components of Heterogeneous Graph Neural Networks (HGNNs), this system reveals intricate links between objects with minimal latency and complexity. 

Decoupling users from the HGNN graph is a crucial tactic that has been used to enable a more in-depth study of item relationships. A multi-link neighbor sampler has also been introduced that improves the effectiveness of the recommendation process. The HGNN model’s computational complexity is greatly decreased by these calculated decisions in conjunction with the 2T component.

Extensive experiments with millions of users have validated the effectiveness of the methodology, exhibiting a notable enhancement in the caliber of customized suggestions. The strategy has resulted in a noteworthy 23% rise in streaming rates and a 46% increase in the rate at which customers are starting new audiobooks. 

The team has summarized their primary contributions as follows.

  1. Examining the Design of Audiobook Recommendation Systems – Extensive research has been conducted on creating a large-scale audiobook recommendation system. The analysis of user consumption patterns allows to better understand consumer preferences for audiobooks, especially when it comes to podcasts, which are renowned for their conversational approach.
  1. Integrating Modular Architecture – A modular design has been suggested that easily incorporates audiobook content into already-in-use recommendation systems. In this architecture, a Two Tower (2T) model and a Heterogeneous Graph Neural Network (HGNN) have been combined into a single stack. While the 2T model easily learns user preferences for audiobooks across all user types, including cold-start users, the HGNN captures long-range, subtle item relations.
  1. Resolving the Imbalance in Data Distribution – An innovative edge sampler has been incorporated into the HGNN to address imbalances in data distribution. The user-audiobook predictions have been generated by integrating weak signals in the user representation. 
  1. Comprehensive Assessment – The 2T-HGNN model has been proven to be efficient and effective through extensive offline trials, consistently outperforming other approaches. Millions of people participating in A/B testing have shown notable gains, such as a 23% rise in audiobook stream rates and a 46% spike in the number of users beginning new audiobooks. 

In conclusion, by utilizing user preferences, sophisticated graph-based methods, and effective computational methodologies, this unique recommendation system tackles the difficulties presented by the integration of audiobooks into the Spotify platform. By doing this, the user experience for audiobooks can be improved while also making a positive impact on the broader richness of the digital audio landscape.


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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.



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