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Front. Appl. Math. Stat. | doi: 10.3389/fams.2019.00044

Deep Learning in Music Recommendation Systems

  • 1Johannes Kepler University of Linz, Austria

Like in many other research areas, deep learning (DL) is increasingly adopted in music recommender systems (MRS). Deep neural networks are used in this area particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists) from music playlists or listening sessions. Latent item factors are commonly integrated into content-based filtering and hybrid MRS, whereas sequence models of music items are used for sequential music recommendation, e.g., automatic playlist continuation.

This review article explains particularities of the music domain in RS research. It gives an overview of the state of the art that employs deep learning for music recommendation. The discussion is structured according to the dimensions of neural network type, input data, recommendation approach (content-based filtering, collaborative filtering, or both), and task (standard or sequential music recommendation). In addition, we discuss major challenges faced in MRS, in particular in the context of the current research on deep learning.

Keywords: Music, recommender systems, music information retrieval, deep learning, neural networks, sequence-aware recommendation, automatic playlist continuation, Survey

Received: 01 Mar 2019; Accepted: 12 Aug 2019.

Edited by:

Michael A. Riegler, Simula Research Laboratory, Norway

Reviewed by:

Junhui Wang, City University of Hong Kong, Hong Kong
Li Su, Academia Sinica, Taiwan  

Copyright: © 2019 Schedl and Schedl. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Markus Schedl, Johannes Kepler University of Linz, Linz, 4040, Upper Austria, Austria, markus.schedl@jku.at
Dr. Markus Schedl, Johannes Kepler University of Linz, Linz, 4040, Upper Austria, Austria, markus.schedl@jku.at