Quick Lists: Enriched Playlist Embeddings for Future Playlist Recommendation
This addresses the problem of improving playlist recommendations for users of digital music services, though it appears incremental as it builds on existing embedding techniques.
The paper tackled the challenge of playlist recommendation by developing a novel method for generating playlist embeddings that are invariant to length and sensitive to track ordering, enriched with user side information, and demonstrated its usefulness for next-best playlist recommendations and cold start problems.
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.