Deep Learning in Music Recommendation Systems
Like in many other research areas, deep learning (DL) is increasingly adopted in music recommendation systems (MRS). Deep neural networks are used in this domain particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music ite...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fams.2019.00044/full |
| _version_ | 1828163834917945344 |
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| author | Markus Schedl |
| author_facet | Markus Schedl |
| author_sort | Markus Schedl |
| collection | DOAJ |
| description | Like in many other research areas, deep learning (DL) is increasingly adopted in music recommendation systems (MRS). Deep neural networks are used in this domain 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. |
| first_indexed | 2024-04-12T01:16:32Z |
| format | Article |
| id | doaj.art-78b26acf1a334b0c9f8fbc2a0a29ce1e |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-12T01:16:32Z |
| publishDate | 2019-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-78b26acf1a334b0c9f8fbc2a0a29ce1e2022-12-22T03:53:57ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872019-08-01510.3389/fams.2019.00044457883Deep Learning in Music Recommendation SystemsMarkus SchedlLike in many other research areas, deep learning (DL) is increasingly adopted in music recommendation systems (MRS). Deep neural networks are used in this domain 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.https://www.frontiersin.org/article/10.3389/fams.2019.00044/fullmusicrecommender systemsmusic information retrievaldeep learningneural networkssequence-aware recommendation |
| spellingShingle | Markus Schedl Deep Learning in Music Recommendation Systems Frontiers in Applied Mathematics and Statistics music recommender systems music information retrieval deep learning neural networks sequence-aware recommendation |
| title | Deep Learning in Music Recommendation Systems |
| title_full | Deep Learning in Music Recommendation Systems |
| title_fullStr | Deep Learning in Music Recommendation Systems |
| title_full_unstemmed | Deep Learning in Music Recommendation Systems |
| title_short | Deep Learning in Music Recommendation Systems |
| title_sort | deep learning in music recommendation systems |
| topic | music recommender systems music information retrieval deep learning neural networks sequence-aware recommendation |
| url | https://www.frontiersin.org/article/10.3389/fams.2019.00044/full |
| work_keys_str_mv | AT markusschedl deeplearninginmusicrecommendationsystems |