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Deezer's Projects

aads_french icon aads_french

Code and data to reproduce the experiments presented in "Automatic Annotation of Direct Speech in Written French Narratives" (ACL 2023)

carousel_bandits icon carousel_bandits

Source code and data from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits" by W. Bendada, G. Salha and T. Bontempelli

concept_hierarchy icon concept_hierarchy

Source code of our ISMIR 2022 paper "Learning Unsupervised Hierarchies of Audio Concepts" by D. Afchar, R. Hennequin and V. Guigue (2022)

consistency icon consistency

Repository for the RecSys 2023 Paper "On the Consistency of Average Embeddings for Item Recommendation"

counsel icon counsel

A collection of advices, ready to use via AOP

crossculturalmusicgenreperception icon crossculturalmusicgenreperception

Python code to reproduce the experiments presented in the article Modeling the Music Genre Perception across Language-Bound Cultures, presented at the EMNLP 2020 conference.

deezer-chromaprint icon deezer-chromaprint

A stand-alone, x86 and x64 compatible, Windows version, of the chromaprint audio library.

elasticmsd icon elasticmsd

Transfer the Million Song Dataset (MSD) in an Elasticsearch index

fastgae icon fastgae

Source code from the article "FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding" by G. Salha, R. Hennequin, J.B. Remy, M. Moussallam and M. Vazirgiannis (2020)

functional_attribution icon functional_attribution

Code of our accepted ICML 2021 paper "Towards Rigorous Interpretations: a Formalisation of Feature Attribution" (D. Afchar, R. Hennequin, V. Guigue)

gracenote2deezer icon gracenote2deezer

An Android sample application, using both GraceNote and Deezer SDK to match your music with Deezer's catalog

gravity_graph_autoencoders icon gravity_graph_autoencoders

Source code from the CIKM 2019 article "Gravity-Inspired Graph Autoencoders for Directed Link Prediction" by G. Salha, S. Limnios, R. Hennequin, V.A. Tran and M. Vazirgiannis

interpretable_nn_attribution icon interpretable_nn_attribution

Source code from our RecSys 2020 paper: "Making neural network interpretable with attribution: application to implicit signals prediction" (D. Afchar, R. Hennequin)

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