| To model or to intervene: A comparison of counterfactual and online learning to rank from user interactions R Jagerman, H Oosterhuis, M de Rijke Proceedings of the 42nd international ACM SIGIR conference on research and …, 2019 | 66 | 2019 |
| When people change their mind: Off-policy evaluation in non-stationary recommendation environments R Jagerman, I Markov, M de Rijke Proceedings of the twelfth ACM international conference on web search and …, 2019 | 52 | 2019 |
| Opensearch: lessons learned from an online evaluation campaign R Jagerman, K Balog, MD Rijke Journal of Data and Information Quality (JDIQ) 10 (3), 1-15, 2018 | 26 | 2018 |
| Learning to rank in theory and practice: from gradient boosting to neural networks and unbiased learning C Lucchese, FM Nardini, RK Pasumarthi, S Bruch, M Bendersky, X Wang, ... Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 24* | 2019 |
| Safe exploration for optimizing contextual bandits R Jagerman, I Markov, MD Rijke ACM Transactions on Information Systems (TOIS) 38 (3), 1-23, 2020 | 18 | 2020 |
| Computing Web-scale Topic Models using an Asynchronous Parameter Server R Jagerman, C Eickhoff, M de Rijke Proceedings of the 40th International ACM SIGIR Conference on Research and …, 2017 | 17 | 2017 |
| Rankt5: Fine-tuning T5 for text ranking with ranking losses H Zhuang, Z Qin, R Jagerman, K Hui, J Ma, J Lu, J Ni, X Wang, ... arXiv preprint arXiv:2210.10634, 2022 | 12 | 2022 |
| Modeling Label Ambiguity for Neural List-Wise Learning to Rank R Jagerman, J Kiseleva, M de Rijke 2nd International Workshop on Neural Information Retrieval (Neu-IR), 2017 | 11 | 2017 |
| The fifteen year struggle of decentralizing privacy-enhancing technology R Jagerman, W Sabee, L Versluis, M de Vos, J Pouwelse arXiv preprint arXiv:1404.4818, 2014 | 11 | 2014 |
| Accelerated Convergence for Counterfactual Learning to Rank R Jagerman, M de Rijke Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 10 | 2020 |
| Bootstrapping recommendations at chrome web store Z Qin, H Zhuang, R Jagerman, X Qian, P Hu, DC Chen, X Wang, ... Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 7 | 2021 |
| Rax: Composable learning-to-rank using JAX R Jagerman, X Wang, H Zhuang, Z Qin, M Bendersky, M Najork Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 6 | 2022 |
| On optimizing top-k metrics for neural ranking models R Jagerman, Z Qin, X Wang, M Bendersky, M Najork Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 4 | 2022 |
| Query-level Ranker Specialization R Jagerman, H Oosterhuis, M de Rijke 1st International Workshop on LEARning Next gEneration Rankers (LEARNER), 2017 | 4 | 2017 |
| Overview of TREC OpenSearch 2017. R Jagerman, K Balog, P Schaer, J Schaible, N Tavakolpoursaleh, ... TREC, 2017 | 4 | 2017 |
| Improving Cloud Storage Search with User Activity. R Jagerman, W Kong, RK Pasumarthi, Z Qin, M Bendersky, M Najork WSDM, 508-516, 2021 | 1 | 2021 |
| Modeling label ambiguity for listwise neural learning to rank R Jagerman, J Kiseleva, M de Rijke Neu-IR, 2017 | 1 | 2017 |
| Query Expansion by Prompting Large Language Models R Jagerman, H Zhuang, Z Qin, X Wang, M Bendersky arXiv preprint arXiv:2305.03653, 2023 | | 2023 |
| Regression Compatible Listwise Objectives for Calibrated Ranking A Bai, R Jagerman, Z Qin, P Kar, BR Lin, X Wang, M Bendersky, M Najork arXiv preprint arXiv:2211.01494, 2022 | | 2022 |
| Efficient, safe and adaptive learning from user interactions R Lagerman Universiteit van Amsterdam, 2020 | | 2020 |
Maarten de RijkeUniversity of Amsterdam & ICAIПотвърден имейл адрес: uva.nl
