13 citations · 14 across the 6 of their papers we have counts for
6 papers
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +3
This paper introduces von Mises-Fisher exploration (vMF-exp), a scalable method for exploring large action sets in reinforcement learning problems where hyperspherical embedding ve…
Text2Playlist: Generating Personalized Playlists from Text on Deezer
Mathieu Delcluze, Antoine Khoury, Clémence Vast +4
The streaming service Deezer heavily relies on the search to help users navigate through its extensive music catalog. Nonetheless, it is primarily designed to find specific items a…
Let's Get It Started: Fostering the Discoverability of New Releases on Deezer
Léa Briand, Théo Bontempelli, Walid Bendada +5
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 feat…
On the Consistency of Average Embeddings for Item Recommendation
Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +2
A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates t…
Track Mix Generation on Music Streaming Services using Transformers
Walid Bendada, Théo Bontempelli, Mathieu Morlon +4
This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists…
A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
Walid Bendada, Guillaume Salha-Galvan, Thomas Bouabça +1
Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their mus…