Music streaming services often aim to recommend songs for users to extend the
playlists they have created on these services. However, extending playlists
while preserving their musical characteristics and matching user preferences
remains a challenging task, commonly referred to as Automatic Playlist
Continuation (APC). Besides, while these services often need to select the best
songs to recommend in real-time and among large catalogs with millions of
candidates, recent research on APC mainly focused on models with few
scalability guarantees and evaluated on relatively small datasets. In this
paper, we introduce a general framework to build scalable yet effective APC
models for large-scale applications. Based on a represent-then-aggregate
strategy, it ensures scalability by design while remaining flexible enough to
incorporate a wide range of representation learning and sequence modeling
techniques, e.g., based on Transformers. We demonstrate the relevance of this
framework through in-depth experimental validation on Spotify's Million
Playlist Dataset (MPD), the largest public dataset for APC. We also describe
how, in 2022, we successfully leveraged this framework to improve APC in
production on Deezer. We report results from a large-scale online A/B test on
this service, emphasizing the practical impact of our approach in such a
real-world application.

本文介绍了在大规模应用中构建可扩展而有效的自动播放列表延续（APC）模型的一般框架，该框架基于表示 - 集成策略，同时保持足够的灵活性以纳入广泛的表示学习和序列建模技术，如变压器。通过详细的实验验证，我们证明了此框架的相关性，强调了它在实际应用中的实际影响。