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This paper provides an outline of the algorithms submitted for the WSDM Cup2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In thechallenge, complete information including acoustic features and userinteraction logs for the first half of a listening session is provided. Ourgoal is to predict whether the individual tracks in the second half of thesession will be skipped or not, only given acoustic features. We proposed twodifferent kinds of algorithms that were based on metric learning and sequencelearning. The experimental results showed that the sequence learning approachperformed significantly better than the metric learning approach. Moreover, weconducted additional experiments to find that significant performance gain canbe achieved using complete user log information.

Sequential Skip Prediction with Few-shot in Streamed Music Contents