Papers › Sequential Skip Prediction with Few-shot in Streamed Music Contents

Sequential Skip Prediction with Few-shot in Streamed Music Contents

24 Jan 2019arXiv:1901.08203archive 2025-07-28

Sungkyun Chang, Seungjin Lee, Kyogu Lee

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.

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Code

mimbres/SeqSkip officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Few-Shot Learning Meta-Learning Metric Learning Sequential skip prediction

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Sequential skip prediction	MSSD	Teacher	mean average accuracy	84.9	#1 of 2	Archive leaderboard	report
Sequential skip prediction	MSSD	seq1HL (2-stack)	mean average accuracy	63.7	#2 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Attention Convolution Dilated Causal Convolution Gated Convolution Gated Linear Unit SNAIL Softmax

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