# Item-Item Music Recommendations With Side Information

## TL;DR

This paper introduces a new item-item recommendation method for music streaming that leverages collaborative filtering and side information, outperforming traditional matrix factorization on a large-scale dataset.

## Contribution

The paper presents a novel approach to compute track similarities using collaborative filtering with side information, improving recommendation quality over existing methods.

## Key findings

- Outperforms implicit matrix factorization on SoundCloud data
- Effective in handling diverse and large-scale music content
- Open-sourced implementation available for related tasks

## Abstract

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.