# Sequential Skip Prediction with Few-shot in Streamed Music Contents

arXiv: https://arxiv.org/abs/1901.08203 · HTML: https://mlsanity.com/paper/1901.08203/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning](https://mlsanity.com/paper/2207.13909/) (2022)
- [CSRec: Rethinking Sequential Recommendation from A Causal Perspective](https://mlsanity.com/paper/2409.05872/) (2024)

## Abstract (arXiv)

This paper provides an outline of the algorithms submitted for the WSDM Cup 2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the challenge, complete information including acoustic features and user interaction logs for the first half of a listening session is provided. Our goal is to predict whether the individual tracks in the second half of the session will be skipped or not, only given acoustic features. We proposed two different kinds of algorithms that were based on metric learning and sequence learning. The experimental results showed that the sequence learning approach performed significantly better than the metric learning approach. Moreover, we conducted additional experiments to find that significant performance gain can be achieved using complete user log information.