Item-Item Music Recommendations With Side Information
This work addresses the challenge of content discovery for users of large-scale music streaming services, though it appears incremental as it builds on existing collaborative filtering techniques.
The paper tackled the problem of generating relevant music recommendations from tens of millions of diverse tracks by presenting a method that computes track-track similarities using collaborative filtering with side information, outperforming implicit matrix factorization on a SoundCloud dataset.
Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of content makes it difficult for a user to find relevant tracks. Relevant recommendations are therefore crucial for a good user experience. Here we present a method to compute track-track similarities using collaborative filtering signals with side information. On a data set from music streaming service SoundCloud, the method here outperforms the widely adopted implicit matrix factorization technique. The implementation of our method is open sourced and can be applied to related item-item recommendation tasks with side information.