Let's Get It Started: Fostering the Discoverability of New Releases on Deezer
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
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| _version_ | 1866909063043874816 |
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| author | Briand, Léa Bontempelli, Théo Bendada, Walid Morlon, Mathieu Rigaud, François Chapus, Benjamin Bouabça, Thomas Salha-Galvan, Guillaume |
| author_facet | Briand, Léa Bontempelli, Théo Bendada, Walid Morlon, Mathieu Rigaud, François Chapus, Benjamin Bouabça, Thomas Salha-Galvan, Guillaume |
| contents | This paper presents our recent initiatives to foster the discoverability of new releases on the music streaming service Deezer. After introducing our search and recommendation features dedicated to new releases, we outline our shift from editorial to personalized release suggestions using cold start embeddings and contextual bandits. Backed by online experiments, we discuss the advantages of this shift in terms of recommendation quality and exposure of new releases on the service. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Let's Get It Started: Fostering the Discoverability of New Releases on Deezer Briand, Léa Bontempelli, Théo Bendada, Walid Morlon, Mathieu Rigaud, François Chapus, Benjamin Bouabça, Thomas Salha-Galvan, Guillaume Information Retrieval Machine Learning This paper presents our recent initiatives to foster the discoverability of new releases on the music streaming service Deezer. After introducing our search and recommendation features dedicated to new releases, we outline our shift from editorial to personalized release suggestions using cold start embeddings and contextual bandits. Backed by online experiments, we discuss the advantages of this shift in terms of recommendation quality and exposure of new releases on the service. |
| title | Let's Get It Started: Fostering the Discoverability of New Releases on Deezer |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2401.02827 |