Towards Seed-Free Music Playlist Generation

Enhancing collaborative Filtering with Playlist Title Information

Conference paper (2018)

Authors

Minz Won Pompeu Fabra University
A. Hanjalic Intelligent Systems
Copyright: © 2018 Jaehun Kim, Minz Won, C.C.S. Liem, A. Hanjalic
DOI
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https://doi.org/10.1145/3267471.3267485
LSTM Collaborative filtering Hybrid recommender system Multi-objective function Music playlist generation WRMF
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Published Date

2018

Language

English

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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.