Title: Sequential Skip Prediction with Few-shot in Streamed Music Contents,
Abstract: 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.