论文标题
快速列表：丰富的播放列表嵌入式未来播放列表推荐
Quick Lists: Enriched Playlist Embeddings for Future Playlist Recommendation
论文作者
论文摘要
在数字音乐服务的背景下，向用户推荐播放列表是一项艰巨的任务，因为播放列表通常比单个部分的总和还多。我们提出了一种新的方法，用于生成播放列表嵌入，这些播放列表嵌入对于播放列表长度且对本地和全球轨道排序敏感。嵌入式还捕获有关播放列表测序的信息，并充满有关播放列表用户的附带信息。我们表明，这些嵌入对于生成次要的播放列表建议很有用，并且该侧面信息可用于冷启动问题。
Recommending playlists to users in the context of a digital music service is a difficult task because a playlist is often more than the mere sum of its parts. We present a novel method for generating playlist embeddings that are invariant to playlist length and sensitive to local and global track ordering. The embeddings also capture information about playlist sequencing, and are enriched with side information about the playlist user. We show that these embeddings are useful for generating next-best playlist recommendations, and that side information can be used for the cold start problem.
