Leveraging Artist Catalogs for Cold-Start Music Recommendation
This addresses the cold-start challenge for music platforms by improving recommendations for new tracks, though it is incremental as it builds on existing methods by incorporating artist hierarchy.
The paper tackles the item cold-start problem in music recommendation by leveraging artist catalogs to provide collaborative signals for new tracks, showing that artist-aware methods more than double Recall and NDCG compared to content-only baselines.
The item cold-start problem poses a fundamental challenge for music recommendation: newly added tracks lack the interaction history that collaborative filtering (CF) requires. Existing approaches often address this problem by learning mappings from content features such as audio, text, and metadata to the CF latent space. However, previous works either omit artist information or treat it as just another input modality, missing the fundamental hierarchy of artists and items. Since most new tracks come from artists with previous history available, we frame cold-start track recommendation as 'semi-cold' by leveraging the rich collaborative signal that exists at the artist level. We show that artist-aware methods can more than double Recall and NDCG compared to content-only baselines, and propose ACARec, an attention-based architecture that generates CF embeddings for new tracks by attending over the artist's existing catalog. We show that our approach has notable advantages in predicting user preferences for new tracks, especially for new artist discovery and more accurate estimation of cold item popularity.