Lucas Maystre
Lucas Maystre
Research Scientist, Spotify
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
Fast and accurate inference of plackett–luce models
L Maystre, M Grossglauser
Advances in neural information processing systems 28, 172-180, 2015
Just sort it! A simple and effective approach to active preference learning
L Maystre, M Grossglauser
Proceedings of ICML 2017, 2017
Collaborative recurrent neural networks for dynamic recommender systems
YJ Ko, L Maystre, M Grossglauser
Asian Conference on Machine Learning, 366-381, 2016
Algorithmic effects on the diversity of consumption on spotify
A Anderson, L Maystre, I Anderson, R Mehrotra, M Lalmas
Proceedings of The Web Conference 2020, 2155-2165, 2020
Mitigating epidemics through mobile micro-measures
M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran
arXiv preprint arXiv:1307.2084, 2013
Contextual and sequential user embeddings for large-scale music recommendation
C Hansen, C Hansen, L Maystre, R Mehrotra, B Brost, F Tomasi, ...
Fourteenth ACM Conference on Recommender Systems, 53-62, 2020
ChoiceRank: Identifying Preferences from Node Traffic in Networks
L Maystre, M Grossglauser
Proceedings of ICML 2017, 2017
The player kernel: learning team strengths based on implicit player contributions
L Maystre, V Kristof, AJG Ferrer, M Grossglauser
arXiv preprint arXiv:1609.01176, 2016
Pairwise Comparisons with Flexible Time-Dynamics
L Maystre, V Kristof, M Grossglauser
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
Can Who-Edits-What Predict Edit Survival?
AB Yardim, V Kristof, L Maystre, M Grossglauser
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
Shifting consumption towards diverse content on music streaming platforms
C Hansen, R Mehrotra, C Hansen, B Brost, L Maystre, M Lalmas
Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021
Efficient Learning from Comparisons
L Maystre
EPFL, 2018
Scalable and Efficient Comparison-based Search without Features
D Chumbalov, L Maystre, M Grossglauser
arXiv preprint arXiv:1905.05049, 2019
A User Study of Perceived Carbon Footprint
V Kristof, V Quelquejay-Leclère, R Zbinden, L Maystre, M Grossglauser, ...
arXiv preprint arXiv:1911.11658, 2019
Homepage and Search Personalization at Spotify
M Tian, R Mehrotra, L Maystre, M Lalmas
DMRN+ 14: Digital Music Research Network One-day Workshop 2019, 2019
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty
J Bütepage, L Maystre, M Lalmas
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
Systems and methods for providing media recommendations using contextual and sequential user embeddings
C Hansen, C Hansen, L Maystre, R Mehrotra, BCP Brost, F Tomasi, ...
US Patent App. 16/789,214, 2021
choix Documentation
L Maystre
Where To Next? A Dynamic Model of User Preferences
F Sanna Passino, L Maystre, D Moor, A Anderson, M Lalmas
Proceedings of the Web Conference 2021, 3210-3220, 2021
Collaborative Classification from Noisy Labels
L Maystre, N Kumarappan, J Bütepage, M Lalmas
International Conference on Artificial Intelligence and Statistics, 1639-1647, 2021
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