| Click models for web search A Chuklin, I Markov, M De Rijke Springer Nature, 2022 | 430 | 2022 |
| A neural click model for web search A Borisov, I Markov, M De Rijke, P Serdyukov Proceedings of the 25th International Conference on World Wide Web, 531-541, 2016 | 175 | 2016 |
| 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 | 59 | 2019 |
| A click sequence model for web search A Borisov, M Wardenaar, I Markov, M De Rijke The 41st International ACM SIGIR Conference on Research & Development in …, 2018 | 57 | 2018 |
| A context-aware time model for web search A Borisov, I Markov, M de Rijke, P Serdyukov Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016 | 44 | 2016 |
| BubbleRank: Safe online learning to re-rank via implicit click feedback C Li, B Kveton, T Lattimore, I Markov, M de Rijke, C Szepesvári, M Zoghi Uncertainty in artificial intelligence, 196-206, 2020 | 37* | 2020 |
| Cascade model-based propensity estimation for counterfactual learning to rank A Vardasbi, M de Rijke, I Markov Proceedings of the 43rd International ACM SIGIR conference on research and …, 2020 | 37 | 2020 |
| Overview of the NTCIR-12 IMine-2 Task. T Yamamoto, Y Liu, M Zhang, Z Dou, K Zhou, I Markov, MP Kato, ... NTCIR 2016, 94-123, 2016 | 34 | 2016 |
| A comparative study of click models for web search A Grotov, A Chuklin, I Markov, L Stout, F Xumara, M de Rijke Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th …, 2015 | 34 | 2015 |
| Distributed information retrieval and applications F Crestani, I Markov Advances in Information Retrieval: 35th European Conference on IR Research …, 2013 | 29 | 2013 |
| Theoretical, qualitative, and quantitative analyses of small-document approaches to resource selection I Markov, F Crestani ACM Transactions on Information Systems (TOIS) 32 (2), 1-37, 2014 | 26 | 2014 |
| Local Variational Feature-Based Similarity Models for Recommending Top-N New Items Y Chen, Y Wang, X Zhao, H Yin, I Markov, MD Rijke ACM Transactions on Information Systems (TOIS) 38 (2), 1-33, 2020 | 22 | 2020 |
| Reducing the uncertainty in resource selection I Markov, L Azzopardi, F Crestani European Conference on Information Retrieval, 507-519, 2013 | 21 | 2013 |
| Manifold learning for rank aggregation S Liang, I Markov, Z Ren, M de Rijke Proceedings of the 2018 World Wide Web Conference, 1735-1744, 2018 | 20 | 2018 |
| 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 |
| Conversational exploratory search via interactive storytelling S Vakulenko, I Markov, M de Rijke arXiv preprint arXiv:1709.05298, 2017 | 17 | 2017 |
| Unsupervised linear score normalization revisited I Markov, A Arampatzis, F Crestani Proceedings of the 35th international ACM SIGIR conference on Research and …, 2012 | 14 | 2012 |
| Image retrieval: Color and texture combining based on query-image I Markov, N Vassilieva International Conference on Image and Signal Processing, 430-438, 2008 | 14 | 2008 |
| Mixture-based correction for position and trust bias in counterfactual learning to rank A Vardasbi, M de Rijke, I Markov Proceedings of the 30th ACM international conference on information …, 2021 | 13 | 2021 |
| MergeDTS: A method for effective large-scale online ranker evaluation C Li, I Markov, MD Rijke, M Zoghi ACM Transactions on Information Systems (TOIS) 38 (4), 1-28, 2020 | 13 | 2020 |
Maarten de RijkeUniversity of Amsterdam & ICAIVerified email at uva.nl
