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.

翻译：物品冷启动问题对音乐推荐构成了根本性挑战：新添加的曲目缺乏协同过滤（CF）所需的交互历史。现有方法通常通过学习从音频、文本和元数据等内容特征到CF潜在空间的映射来解决这一问题。然而，以往的工作要么忽略了艺术家信息，要么仅将其视为另一种输入模态，从而错失了艺术家与物品之间的基本层次结构。由于大多数新曲目来自已有历史记录的艺术家，我们将冷启动曲目推荐构建为“半冷启动”问题，通过利用艺术家层面存在的丰富协同信号来解决。我们表明，与仅基于内容的方法相比，艺术家感知方法可以将召回率和NDCG提升一倍以上，并提出ACARec——一种基于注意力机制的架构，通过关注艺术家现有作品目录来生成新曲目的CF嵌入。我们证明，该方法在预测用户对新曲目的偏好方面具有显著优势，尤其是在发现新艺术家以及更准确估计冷启动物品流行度方面。