| Discovering key concepts in verbose queries M Bendersky, WB Croft Proceedings of the 31st annual international ACM SIGIR conference on …, 2008 | 327 | 2008 |
| 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 | 219 | 2010 |
| 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 | 200 | 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 | 184 | 2018 |
| Quality-biased ranking of web documents M Bendersky, WB Croft, Y Diao Proceedings of the fourth ACM international conference on Web search and …, 2011 | 144 | 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 | 135 | 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 | 129 | 2011 |
| 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 | 119 | 2012 |
| 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 | 108 | 2019 |
| Finding text reuse on the web M Bendersky, WB Croft Proceedings of the Second ACM International Conference on Web Search and …, 2009 | 106 | 2009 |
| Information retrieval with verbose queries M Gupta, M Bendersky Foundations and Trends® in Information Retrieval 9 (3-4), 209-354, 2015 | 92 | 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 | 87 | 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 | 86 | 2018 |
| 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 | 66 | 2019 |
| Learning-to-Rank with BERT in TF-Ranking S Han, X Wang, M Bendersky, M Najork arXiv preprint arXiv:2004.08476, 2020 | 65 | 2020 |
| Situational context for ranking in personal search H Zamani, M Bendersky, X Wang, M Zhang Proceedings of the 26th International Conference on World Wide Web, 1531-1540, 2017 | 65 | 2017 |
| 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 | 62 | 2019 |
| Two-stage learning to rank for information retrieval V Dang, M Bendersky, WB Croft European Conference on Information Retrieval, 423-434, 2013 | 58 | 2013 |
| Utilizing passage-based language models for document retrieval M Bendersky, O Kurland European Conference on Information Retrieval, 162-174, 2008 | 56 | 2008 |
| Two-stage query segmentation for information retrieval M Bendersky, WB Croft, DA Smith Proceedings of the 32nd international acm sigir conference on research and …, 2009 | 54 | 2009 |
Marc NajorkGoogleVerified email at google.com
