# Large Music Recommendation Studies for Small Teams

arXiv: https://arxiv.org/abs/2301.13388 · HTML: https://mlsanity.com/paper/2301.13388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)

## Abstract (arXiv)

Running live music recommendation studies without direct industry partnerships can be a prohibitively daunting task, especially for small teams. In order to help future researchers interested in such evaluations, we present a number of struggles we faced in the process of generating our own such evaluation system alongside potential solutions. These problems span the topics of users, data, computation, and application architecture.