Machine Learning Researcher
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gsalhagalvan [at] gmail [dot] com
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I am a French researcher specializing in Graph-Based Machine Learning and Recommender Systems, with a Ph.D. from École Polytechnique and over seven years of experience in the music tech industry.
Until recently, I was a Research Scientist and Coordinator on Music Recommendation at Deezer. From 2018 to 2024, I conducted research on Machine/Deep Learning, Graph Representation Learning, Information Retrieval, and Recommender Systems as part of the Research team in France. Previously, from 2016 to 2018, I worked as a Data Scientist in the company, collaborating with cross-functional teams to enhance the user experience.
My research has resulted in over 30 scientific publications in peer-reviewed journals and international conferences, earning best paper awards or honorable mentions at RecSys 2020, 2021, and 2024, as well as at ECIR 2024. Several of these publications describe algorithms now powering Deezer's recommender systems, helping millions of users worldwide discover new music daily.
I completed my Ph.D. from 2018 to 2022 alongside my work at Deezer, through a scientific partnership with École Polytechnique. I was supervised by Romain Hennequin and Michalis Vazirgiannis.
Prior to that, I graduated in 2016 from ENS Paris-Saclay with an M.Sc. in Machine Learning (MVA), and from ENSAE Paris with an M.Eng. in Data Science, both with first-class honours.
I am now embarking on the next chapter of my career and look forward to sharing more soon!
See me also on Google Scholar and ORCID.org.
EARL: The 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models
I. Li, R. Dong, G. Salha-Galvan, A. Lawlor, D. Liu, L. Li
19th ACM Conference on Recommender Systems (RecSys 2025)
[PDF (soon)] [Website (soon)]
von Mises-Fisher Sampling of GloVe Vectors
W. Bendada, G. Salha-Galvan, R. Hennequin, T. Bontempelli, T. Bouabça, T. Cazenave
Workshop on Frontiers in Probabilistic Inference: Sampling Meets Learning
13th International Conference on Learning Representations (ICLR 2025)
[PDF] [Code]
To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
G. Salha-Galvan, J. Xu
2025 ACM Web Conference (WWW 2025)
[PDF] [Code] [Data]
Note: publications from 2023 and 2024 reflect my Research Coordinator role at Deezer, where I was responsible for supervising projects. I am often listed as the second or last author in these articles.
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
V.A. Tran, G. Salha-Galvan, B. Sguerra, R. Hennequin
18th ACM Conference on Recommender Systems (RecSys 2024)
[PDF] [Code] [Data] Best full paper candidate
Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data
K. Matrosova, L. Marey, G. Salha-Galvan, T. Louail, O. Bodini, M. Moussallam
18th ACM Conference on Recommender Systems (RecSys 2024)
[PDF] [Code] [Data]
Let’s Get It Started (Again!): Fostering the Discoverability of New Releases on Deezer
L. Briand, T. Bontempelli, W. Bendada, M. Morlon, F. Rigaud, B. Chapus, G. Salha-Galvan
Workshop on Music Recommender Systems
18th ACM Conference on Recommender Systems (RecSys 2024)
(Workshop presentation showcasing our ECIR 2024 work)
[PDF] [Slides]
vMF-exp: von Mises-Fisher Exploration of Large Action Sets with Hyperspherical Embeddings
W. Bendada, G. Salha-Galvan, R. Hennequin, T. Bontempelli, T. Bouabça, T. Cazenave
Workshop on Aligning Reinforcement Learning Experimentalists and Theorists
41st International Conference on Machine Learning (ICML 2024)
[PDF] [Code (soon)]
Let’s Get It Started: Fostering the Discoverability of New Releases on Deezer
L. Briand, T. Bontempelli, W. Bendada, M. Morlon, F. Rigaud, B. Chapus, G. Salha-Galvan
46th European Conference on Information Retrieval (ECIR 2024)
[PDF] [Slides]
Best industry track paper award
Track Mix Generation on Music Streaming Services using Transformers
W. Bendada, T. Bontempelli, M. Morlon, B. Chapus, T. Cador, T. Bouabça, G. Salha-Galvan
17th ACM Conference on Recommender Systems (RecSys 2023)
[PDF] [Blog Post]
On the Consistency of Average Embeddings for Item Recommendation
W. Bendada, G. Salha-Galvan, T. Bouabça, T. Cazenave
17th ACM Conference on Recommender Systems (RecSys 2023)
[PDF] [Code] [Poster]
Attention Mixtures for Time-Aware Sequential Recommendation
V.A. Tran, G. Salha-Galvan, B. Sguerra, R. Hennequin
46th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2023)
[PDF] [Code]
A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
W. Bendada, G. Salha-Galvan, T. Bouabça, T. Cazenave
46th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2023)
[PDF] [Code]
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
G. Salha-Galvan, J.F. Lutzeyer, G. Dasoulas, R. Hennequin, M. Vazirgiannis
Workshop on New Frontiers in Graph Learning
36th Conference on Neural Information Processing Systems (NeurIPS 2022)
[PDF] [Code]
Flow Moods: Recommending Music by Moods on Deezer
T. Bontempelli, B. Chapus, F. Rigaud, M. Morlon, M. Lorant, G. Salha-Galvan
16th ACM Conference on Recommender Systems (RecSys 2022)
[PDF]
[Blog Post]
[Poster]
[Video]
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
G. Salha-Galvan
Ph.D. thesis, École Polytechnique, Institut Polytechnique de Paris
[PDF] [Slides from the defense]
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
G. Salha-Galvan, J.F. Lutzeyer, G. Dasoulas, R. Hennequin, M. Vazirgiannis
Neural Networks 153, 474-495, Elsevier (2021 impact factor: 9.657)
[PDF] [Code]
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
G. Salha-Galvan, R. Hennequin, B. Chapus, V.A. Tran, M. Vazirgiannis
15th ACM Conference on Recommender Systems (RecSys 2021)
[PDF]
[Code]
[Data] [Video] Best student paper honorable mention
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
V.A. Tran, G. Salha-Galvan, R. Hennequin, M. Moussallam
15th ACM Conference on Recommender Systems (RecSys 2021)
[PDF] [Code] [Video]
A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
L. Briand, G. Salha-Galvan, W. Bendada, M. Morlon, V.A. Tran
27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)
[PDF]
[Blog Post]
[Code] [Data] [Video]
Modéliser la Perception des Genres Musicaux à travers Différentes Cultures à partir de Ressources Linguistiques
E.V. Epure, G. Salha-Galvan, M.Moussallam, R. Hennequin
28ème Conférence sur le Traitement Automatique des Langues Naturelles (TALN 2021)
(Summary paper presenting our EMNLP 2020 work)
[PDF] [Code]
Note: I published under the name G. Salha until 2020.
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
G. Salha, R. Hennequin, J.B. Remy, M. Moussallam, M. Vazirgiannis
Neural Networks 142, 1-19, Elsevier (2020 impact factor: 8.05)
[PDF] [Code]
Modeling the Music Genre Perception across Language-Bound Cultures
E.V. Epure, G. Salha, M. Moussallam, R. Hennequin
2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)
[PDF] [Code] [Data] [Video]
Carousel Personalization in Music Streaming Apps with Contextual Bandits
W. Bendada, G. Salha, T. Bontempelli
14th ACM Conference on Recommender Systems (RecSys 2020)
[PDF] [Code] [Data] [Video] Best short paper honorable mention
Simple and Effective Graph Autoencoders with One-Hop Linear Models
G. Salha, R. Hennequin, M. Vazirgiannis
2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020)
[PDF] [Code]
Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
E.V. Epure, G. Salha, R. Hennequin
21st International Society for Music Information Retrieval Conference (ISMIR 2020)
[PDF] [Code] [Data]
Muzeeglot : Annotation Multilingue et Multi-Sources d’Entités Musicales à partir de Représentations de Genres Musicaux
E.V. Epure, G. Salha, F. Voituret, M. Baranes, R. Hennequin
27ème Conférence sur le Traitement Automatique des Langues Naturelles (TALN 2020)
[PDF (in French)] [Slides (in English)] [Code]
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
G. Salha, R. Hennequin, M. Vazirgiannis
Workshop on Graph Representation Learning
33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
[PDF] [Code]
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
G. Salha, S. Limnios, R. Hennequin, V.A. Tran, M. Vazirgiannis
28th ACM International Conference on Information and Knowledge Management (CIKM 2019)
[PDF] [Code]
A Degeneracy Framework for Scalable Graph Autoencoders
G. Salha, R. Hennequin, V.A. Tran, M. Vazirgiannis
28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
[PDF] [Code]