Representation, Exploration and Recommendation of Playlists

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Playlists have become a significant part of our listening experience because of digital cloud-based services such as Spotify, Pandora, Apple Music, making playlist recommendation crucial to music services today. With an aim towards playlist discovery and recommendation, we leverage sequence-to-sequence modeling to learn a fixed-length representation of playlists in an unsupervised manner. We evaluate our work using a recommendation task, along with embedding-evaluation tasks, to study the extent to which semantic characteristics such as genre, song-order, etc. are captured by the playlist embeddings and how they can be leveraged for music recommendation.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
EditorsPeggy Cellier, Kurt Driessens
PublisherSpringer
Pages543-550
Number of pages8
ISBN (Print)9783030438869
DOIs
StatePublished - 2020
Event19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany
Duration: Sep 16 2019Sep 20 2019

Publication series

NameCommunications in Computer and Information Science
Volume1168 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/TerritoryGermany
CityWurzburg
Period9/16/199/20/19

Keywords

  • Playlists
  • Recommendation
  • Sequence-to-sequence

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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