TY - GEN
T1 - Using adversarial autoencoders for multi-modal automatic playlist continuation
AU - Vagliano, Iacopo
AU - Galke, Lukas
AU - Mai, Florian
AU - Scherp, Ansgar
N1 - Funding Information: This work was supported by the German Research Foundation under project number 311018540 (Linked Open Citation Database) as well as by the EU H2020 project MOVING (contract no 693092). Publisher Copyright: © 2018 Association for Computing Machinery.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.
AB - The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.
KW - Adversarial autoencoders
KW - Automatic playlist continuation
KW - Multi-modal recommender
KW - Music recommender systems
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85056731617&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3267471.3267476
DO - https://doi.org/10.1145/3267471.3267476
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the ACM Recommender Systems Challenge 2018, RecSys Challenge 2018
PB - Association for Computing Machinery
T2 - 12th ACM Recommender Systems Challenge Workshop, RecSys Challenge 2018
Y2 - 2 October 2018 through 2 October 2018
ER -