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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.

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Contextualizing Spotify’s Audiobook List Recommendations with Descriptive Shelves

  • 会议论文

  • 发表日期：

    2025年

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  • Gustavo Penha,
  • Alice Wang,
  • Martin Achenbach,
  • Kristen Sheets,
  • Sahitya Mantravadi,
  • Remi Galvez,
  • Nico Guetta-Jeanrenaud,
  • Divya Narayanan,
  • Ofeliya Kalaydzhyan,
  • Hugues Bouchard

摘要

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.