| 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 | 268 | 2020 |
| Fast and accurate inference of Plackett–Luce models L Maystre, M Grossglauser NeurIPS 2015, 2015 | 168 | 2015 |
| Contextual and sequential user embeddings for large-scale music recommendation C Hansen, C Hansen, L Maystre, R Mehrotra, B Brost, F Tomasi, ... RecSys 2020, 53-62, 2020 | 147 | 2020 |
| Just sort it! A simple and effective approach to active preference learning L Maystre, M Grossglauser ICML 2017, 2017 | 85* | 2017 |
| Collaborative recurrent neural networks for dynamic recommender systems YJ Ko, L Maystre, M Grossglauser ACML 2016, 2016 | 72 | 2016 |
| 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 | 47 | 2021 |
| 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 | 31 | 2021 |
| ChoiceRank: Identifying Preferences from Node Traffic in Networks L Maystre, M Grossglauser ICML 2017, 2017 | 22 | 2017 |
| 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 | 21 | 2022 |
| Pairwise Comparisons with Flexible Time-Dynamics L Maystre, V Kristof, M Grossglauser Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 20 | 2019 |
| Impatient bandits: Optimizing recommendations for the long-term without delay TM McDonald, L Maystre, M Lalmas, D Russo, K Ciosek Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 19 | 2023 |
| Mitigating epidemics through mobile micro-measures M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran arXiv preprint arXiv:1307.2084, 2013 | 19 | 2013 |
| 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 | 11 | 2022 |
| Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding G Van Goffrier, L Maystre, CM Gilligan-Lee Conference on Causal Learning and Reasoning, 791-813, 2023 | 10 | 2023 |
| Optimizing audio recommendations for the long-term: A reinforcement learning perspective L Maystre, D Russo, Y Zhao arXiv preprint arXiv:2302.03561, 2023 | 10 | 2023 |
| Scalable and efficient comparison-based search without features D Chumbalov, L Maystre, M Grossglauser ICML 2020, 2020 | 9* | 2020 |
| 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 | 8 | 2018 |
| Temporally-consistent survival analysis L Maystre, D Russo Advances in Neural Information Processing Systems 35, 10671-10683, 2022 | 7 | 2022 |
| Choix—Inference algorithms for models based on Luce’s choice axiom L Maystre URL https://pypi. python. org/pypi/choix, python package version 0.3 2, 2018 | 7 | 2018 |
| On the importance of uncertainty in decision-making with large language models N Felicioni, L Maystre, S Ghiassian, K Ciosek arXiv preprint arXiv:2404.02649, 2024 | 6 | 2024 |
Mounia LalmasSpotifyVerified email at acm.org
