| Discovering key concepts in verbose queries M Bendersky, WB Croft Proceedings of the 31st annual international ACM SIGIR conference on …, 2008 | 337 | 2008 |
| Learning to rank with selection bias in personal search X Wang, M Bendersky, D Metzler, M Najork Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016 | 263 | 2016 |
| Position bias estimation for unbiased learning to rank in personal search X Wang, N Golbandi, M Bendersky, D Metzler, M Najork Proceedings of the eleventh ACM international conference on web search and …, 2018 | 261 | 2018 |
| Learning concept importance using a weighted dependence model M Bendersky, D Metzler, WB Croft Proceedings of the third ACM international conference on Web search and data …, 2010 | 227 | 2010 |
| Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning K Srinivasan, K Raman, J Chen, M Bendersky, M Najork Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 168 | 2021 |
| Quality-biased ranking of web documents M Bendersky, WB Croft, Y Diao Proceedings of the fourth ACM international conference on Web search and …, 2011 | 147 | 2011 |
| Analysis of long queries in a large scale search log M Bendersky, WB Croft Proceedings of the 2009 workshop on Web Search Click Data, 8-14, 2009 | 141 | 2009 |
| Parameterized concept weighting in verbose queries M Bendersky, D Metzler, WB Croft Proceedings of the 34th international ACM SIGIR conference on Research and …, 2011 | 136 | 2011 |
| Tf-ranking: Scalable tensorflow library for learning-to-rank RK Pasumarthi, S Bruch, X Wang, C Li, M Bendersky, M Najork, J Pfeifer, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 133 | 2019 |
| Effective query formulation with multiple information sources M Bendersky, D Metzler, WB Croft Proceedings of the fifth ACM international conference on Web search and data …, 2012 | 127 | 2012 |
| The lambdaloss framework for ranking metric optimization X Wang, C Li, N Golbandi, M Bendersky, M Najork Proceedings of the 27th ACM international conference on information and …, 2018 | 125 | 2018 |
| Finding text reuse on the web M Bendersky, WB Croft Proceedings of the Second ACM International Conference on Web Search and …, 2009 | 110 | 2009 |
| Information retrieval with verbose queries M Gupta, M Bendersky Proceedings of the 38th International ACM SIGIR Conference on Research and …, 2015 | 102 | 2015 |
| Modeling higher-order term dependencies in information retrieval using query hypergraphs M Bendersky, WB Croft Proceedings of the 35th international ACM SIGIR conference on Research and …, 2012 | 96 | 2012 |
| Semantic text matching for long-form documents JY Jiang, M Zhang, C Li, M Bendersky, N Golbandi, M Najork The world wide web conference, 795-806, 2019 | 93 | 2019 |
| Information redaction from document data M Bendersky, V Josifovski, A Saikia, MA Cartright, J Yang, LG Pueyo, ... US Patent 9,734,148, 2017 | 92 | 2017 |
| Learning-to-Rank with BERT in TF-Ranking S Han, X Wang, M Bendersky, M Najork arXiv preprint arXiv:2004.08476, 2020 | 90 | 2020 |
| Addressing trust bias for unbiased learning-to-rank A Agarwal, X Wang, C Li, M Bendersky, M Najork The World Wide Web Conference, 4-14, 2019 | 84 | 2019 |
| Are neural rankers still outperformed by gradient boosted decision trees? Z Qin, L Yan, H Zhuang, Y Tay, RK Pasumarthi, X Wang, M Bendersky, ... | 82 | 2021 |
| Learning groupwise multivariate scoring functions using deep neural networks Q Ai, X Wang, S Bruch, N Golbandi, M Bendersky, M Najork Proceedings of the 2019 ACM SIGIR international conference on theory of …, 2019 | 81 | 2019 |
Marc NajorkGoogleVerified email at google.com
