Towards Seed-Free Music Playlist Generation
Enhancing collaborative Filtering with Playlist Title Information
Jeahun Kim (TU Delft - Multimedia Computing)
Minz Won (Pompeu Fabra University)
Cynthia C.S. Liem (TU Delft - Multimedia Computing)
A Hanjalic (TU Delft - Intelligent Systems)
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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.