Documents

  • a14-Kim

    Final published version, 848 KB, PDF-document

DOI

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
Event12th ACM Recommender Systems Challenge Workshop, RecSys Challenge 2018 - Vancouver, Canada
Duration: 2 Oct 20182 Oct 2018

Conference

Conference12th ACM Recommender Systems Challenge Workshop, RecSys Challenge 2018
CountryCanada
CityVancouver
Period2/10/182/10/18

    Research areas

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

ID: 47580617