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Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation

Published: 02 October 2018 Publication History

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 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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Cited By

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  • (2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
  • (2022)Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalitiesInformation Retrieval Journal10.1007/s10791-022-09408-925:3(269-305)Online publication date: 4-Apr-2022
  • (2021)A Survey on Adversarial Recommender SystemsACM Computing Surveys10.1145/343972954:2(1-38)Online publication date: 5-Mar-2021
  • Show More Cited By

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cover image ACM Other conferences
RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018
October 2018
96 pages
ISBN:9781450365864
DOI:10.1145/3267471
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 October 2018

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Author Tags

  1. adversarial autoencoders
  2. automatic playlist continuation
  3. multi-modal recommender
  4. music recommender systems
  5. neural networks

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RecSys Challenge '18

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Overall Acceptance Rate 11 of 15 submissions, 73%

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Cited By

View all
  • (2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
  • (2022)Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalitiesInformation Retrieval Journal10.1007/s10791-022-09408-925:3(269-305)Online publication date: 4-Apr-2022
  • (2021)A Survey on Adversarial Recommender SystemsACM Computing Surveys10.1145/343972954:2(1-38)Online publication date: 5-Mar-2021
  • (2019)An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist ContinuationACM Transactions on Intelligent Systems and Technology10.1145/334425710:5(1-21)Online publication date: 18-Sep-2019

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