Lucas Maystre
Lucas Maystre
Research Scientist, Spotify
Verified email at - Homepage
Cited by
Cited by
Fast and accurate inference of Plackett–Luce models
L Maystre, M Grossglauser
NeurIPS 2015, 2015
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
Just sort it! A simple and effective approach to active preference learning
L Maystre, M Grossglauser
ICML 2017, 2017
Collaborative recurrent neural networks for dynamic recommender systems
YJ Ko, L Maystre, M Grossglauser
ACML 2016, 2016
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
Mitigating epidemics through mobile micro-measures
M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran
arXiv preprint arXiv:1307.2084, 2013
ChoiceRank: Identifying Preferences from Node Traffic in Networks
L Maystre, M Grossglauser
ICML 2017, 2017
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
Pairwise Comparisons with Flexible Time-Dynamics
L Maystre, V Kristof, M Grossglauser
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
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
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
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
Scalable and Efficient Comparison-based Search without Features
D Chumbalov, L Maystre, M Grossglauser
arXiv preprint arXiv:1905.05049, 2019
Efficient learning from comparisons
L Maystre
EPFL, 2018
Collaborative Classification from Noisy Labels
L Maystre, N Kumarappan, J Bütepage, M Lalmas
AISTATS 2021, 2021
A User Study of Perceived Carbon Footprint
V Kristof, V Quelquejay-Leclère, R Zbinden, L Maystre, M Grossglauser, ...
arXiv preprint arXiv:1911.11658, 2019
Fast and Accurate Inference of Plackett–Luce Models Supplementary Material
L Maystre, M Grossglauser
The Dynamics of Exploration on Spotify
L Mok, SF Way, L Maystre, A Anderson
Proceedings of the International AAAI Conference on Web and Social Media 16 …, 2022
Using Survival Models to Estimate User Engagement in Online Experiments
P Chandar, B St. Thomas, L Maystre, V Pappu, R Sanchis-Ojeda, T Wu, ...
Proceedings of the ACM Web Conference 2022, 3186-3195, 2022
Multistate Analysis with Infinite Mixtures of Markov Chains
L Maystre, T Wu, RS Ojeda, T Jebara
UAI 2022, 2022
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