Playlist Continuation

Playlist continuation aims to algorithmically extend user-created music playlists, maintaining musical coherence and personalized preferences. Current research focuses on overcoming the "cold-start" problem—recommending songs with limited interaction data—through multi-modal models that integrate audio and textual features, often employing contrastive learning and transformer-based architectures for scalability. These advancements are crucial for improving the user experience on music streaming services, as demonstrated by successful real-world deployments and A/B testing on large datasets.