Sequential Skip Prediction with Few-shot in Streamed Music Contents
This work addresses the incremental challenge of skip prediction for music streaming services like Spotify, with potential applications in recommendation systems.
The paper tackled the problem of predicting track skips in the second half of a music listening session using acoustic features, finding that a sequence learning approach outperformed metric learning and that using complete user logs significantly improved performance.
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.