Contextualizing Spotify's Audiobook List Recommendations with Descriptive Shelves

In this paper, we propose a pipeline to generate contextualized list recommendations with descriptive shelves in the domain of audiobooks. By creating several shelves for topics the user has an affinity to, e.g. Uplifting Women's Fiction, we can help them explore their recommendations according to their interests and at the same time recommend a diverse set of items. To do so, we use Large Language Models (LLMs) to enrich each item's metadata based on a taxonomy created for this domain. Then we create diverse descriptive shelves for each user. A/B tests show improvements in user engagement and audiobook discovery metrics, demonstrating benefits for users and content creators.

@article{penha2025_2504.13572,
  title={ Contextualizing Spotify's Audiobook List Recommendations with Descriptive Shelves },
  author={ Gustavo Penha and Alice Wang and Martin Achenbach and Kristen Sheets and Sahitya Mantravadi and Remi Galvez and Nico Guetta-Jeanrenaud and Divya Narayanan and Ofeliya Kalaydzhyan and Hugues Bouchard },
  journal={arXiv preprint arXiv:2504.13572},
  year={ 2025 }
}