publication . Conference object . 2018

Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation

Vagliano, Iacopo; Galke, Lukas; Florian, Mai; Scherp, Ansgar;
Open Access
  • Published: 02 Oct 2018
  • Publisher: ACM Press
Abstract
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 the current playlist. The ACM Recommender Systems Challenge 2018 focuses on such a 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 pla...
Subjects
ACM Computing Classification System: InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI)
free text keywords: music recommender systems, neural networks, adversarial autoencoders, multi-modal recommender, automatic playlist continuation, User engagement, Active listening, Machine learning, computer.software_genre, computer, Modalities, Information retrieval, Artificial intelligence, business.industry, business, Adversarial system, Artificial neural network, Modal, Computer science, Continuation, Recommender system
Related Organizations
Funded by
EC| MOVING
Project
MOVING
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
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publication . Conference object . 2018

Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation

Vagliano, Iacopo; Galke, Lukas; Florian, Mai; Scherp, Ansgar;