# Evaluating Music Recommendations with Binary Feedback for Multiple Stakeholders

arXiv: https://arxiv.org/abs/2109.07692 · HTML: https://mlsanity.com/paper/2109.07692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Negative Feedback for Music Personalization](https://mlsanity.com/paper/2406.04488/) (2024)

## Abstract (arXiv)

High quality user feedback data is essential to training and evaluating a successful music recommendation system, particularly one that has to balance the needs of multiple stakeholders. Most existing music datasets suffer from noisy feedback and self-selection biases inherent in the data collected by music platforms. Using the Piki Music dataset of 500k ratings collected over a two-year time period, we evaluate the performance of classic recommendation algorithms on three important stakeholders: consumers, well-known artists and lesser-known artists. We show that a matrix factorization algorithm trained on both likes and dislikes performs significantly better compared to one trained only on likes for all three stakeholders.