Towards Seed-Free Music Playlist Generation: Enhancing collaborative Filtering with Playlist Title Information

Jaehun Kim, Minz Won, Cynthia C.S. Liem, Alan Hanjalic

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)
75 Downloads (Pure)

Abstract

In this paper, we propose a hybrid Neural Collaborative Filtering (NCF) model trained with a multi-objective function to achieve a music playlist generation system. The proposed approach focuses particularly on the cold-start problem (playlists with no seed tracks) and uses a text encoder employing a Recurrent Neural Network (RNN) to exploit textual information given by the playlist title. To accelerate the training, we first apply Weighted Regularized Matrix Factorization (WRMF) as the basic recommendation model to prelearn latent factors of playlists and tracks. These factors then feed into the proposed multi-objective optimization that also involves embeddings of playlist titles. The experimental study indicates that the proposed approach can effectively suggest suitable music tracks for a given playlist title, compensating poor original recommendation results made on empty playlists by the WRMF model.

Original languageEnglish
Title of host publicationRecSys Challenge '18
Subtitle of host publicationProceedings of the ACM Recommender Systems Challenge 2018
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1-6
Number of pages6
ISBN (Electronic)978-1-4503-6586-4
DOIs
Publication statusPublished - 2018
EventWorkshop on Recommenders in Tourism
co-located with the 12th ACM Conference on Recommender Systems (RecSys 2018)
- Vancouver, Canada
Duration: 7 Oct 20187 Oct 2018

Workshop

WorkshopWorkshop on Recommenders in Tourism
co-located with the 12th ACM Conference on Recommender Systems (RecSys 2018)
Country/TerritoryCanada
CityVancouver
Period7/10/187/10/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Collaborative filtering
  • Hybrid recommender system
  • LSTM
  • Multi-objective function
  • Music playlist generation
  • WRMF

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