| 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 | 143 | 2020 |
| Fast and accurate inference of Plackett–Luce models L Maystre, M Grossglauser NeurIPS 2015, 2015 | 123 | 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 | 87 | 2020 |
| Just sort it! A simple and effective approach to active preference learning L Maystre, M Grossglauser ICML 2017, 2017 | 70* | 2017 |
| Collaborative recurrent neural networks for dynamic recommender systems YJ Ko, L Maystre, M Grossglauser ACML 2016, 2016 | 60 | 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 | 24 | 2021 |
| Mitigating epidemics through mobile micro-measures M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran arXiv preprint arXiv:1307.2084, 2013 | 20 | 2013 |
| ChoiceRank: Identifying Preferences from Node Traffic in Networks L Maystre, M Grossglauser ICML 2017, 2017 | 16 | 2017 |
| Pairwise Comparisons with Flexible Time-Dynamics L Maystre, V Kristof, M Grossglauser Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 12 | 2019 |
| 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 | 10* | 2021 |
| 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 | 7 | 2016 |
| 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 | 6 | 2022 |
| 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 | 6 | 2018 |
| Scalable and efficient comparison-based search without features D Chumbalov, L Maystre, M Grossglauser ICML 2020, 2020 | 5* | 2020 |
| Choix—Inference algorithms for models based on Luce’s choice axiom L Maystre URL https://github. com/lucasmaystre/choix, 2017 | 5 | 2017 |
| Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty J Bütepage, L Maystre, M Lalmas ECML-PKDD 2021, 2021 | 4 | 2021 |
| Efficient learning from comparisons L Maystre EPFL, 2018 | 4 | 2018 |
| A strong baseline for batch imitation learning M Smith, L Maystre, Z Dai, K Ciosek arXiv preprint arXiv:2302.02788, 2023 | 3 | 2023 |
| Temporally-consistent survival analysis L Maystre, D Russo Advances in Neural Information Processing Systems 35, 10671-10683, 2022 | 3 | 2022 |
| Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective L Maystre, D Russo, Y Zhao arXiv preprint arXiv:2302.03561, 2023 | 2 | 2023 |
Matthias GrossglauserEPFLepfl.ch의 이메일 확인됨
