Using adversarial autoencoders for multi-modal automatic playlist continuation

Vagliano, I., Galke, L., Mai, F., & Scherp, A. (2018). Using adversarial autoencoders for multi-modal automatic playlist continuation. In C.-W. Chen, P. Lamere, M. Schedl, & H. Zamani (Eds.), RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018 (pp. 5.1-5.6). New York: ACM. doi:10.1145/3267471.3267476.
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.
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