Towards Seed-Free Music Playlist Generation

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

Kim, Jaehun (TU Delft Multimedia Computing)
Won, Minz (Pompeu Fabra University)
Liem, C.C.S. (TU Delft Multimedia Computing)
Hanjalic, A. (TU Delft Intelligent Systems)

Intelligent Systems

2018

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.

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

http://resolver.tudelft.nl/uuid:ec345852-ae51-436c-b87a-63b468bbf15a

https://doi.org/10.1145/3267471.3267485

Association for Computing Machinery (ACM), New York

2019-04-02

978-1-4503-6586-4

RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

Workshop on Recommenders in Tourism
co-located with the 12th ACM Conference on Recommender Systems (RecSys 2018), 2018-10-07, Vancouver, Canada

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.

Institutional Repository

conference paper

© 2018 Jaehun Kim, Minz Won, C.C.S. Liem, A. Hanjalic