└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Learning To Rank (LTR) 2 | 3 | ## Papers 4 | 5 | ### 2003 6 | 7 | - Freund, Yoav, et al. "[An efficient boosting algorithm for combining preferences.](https://www.jmlr.org/papers/volume4/freund03a/freund03a.pdf)" Journal of machine learning research 4.Nov (2003): 933-969. 8 | 9 | ### 2005 10 | 11 | - Burges, Chris, et al. "[Learning to rank using gradient descent.](https://www.researchgate.net/profile/Christopher_Burges/publication/221345726_Learning_to_Rank_using_Gradient_Descent/links/00b49518c11a6cbcb8000000.pdf)" Proceedings of the 22nd international conference on Machine learning. 2005. 12 | 13 | ### 2007 14 | 15 | - Xu, Jun, and Hang Li. "[Adarank: a boosting algorithm for information retrieval.](http://www.bigdatalab.ac.cn/~junxu/publications/SIGIR2007_AdaRank.pdf)" Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007. 16 | - Yue, Yisong, et al. "[A support vector method for optimizing average precision.](http://www.cs.cornell.edu/~tj/publications/yue_etal_07a.pdf)" Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007. 17 | - Geng, Xiubo, et al. "[Feature selection for ranking.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.480.3451&rep=rep1&type=pdf)" Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007. 18 | - Tsai, Ming-Feng, et al. "[FRank: a ranking method with fidelity loss.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2006-155.pdf)" Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007. 19 | - Cao, Zhe, et al. "[Learning to rank: from pairwise approach to listwise approach.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.333.4334&rep=rep1&type=pdf)" Proceedings of the 24th international conference on Machine learning. 2007. 20 | - Burges, Christopher J., Robert Ragno, and Quoc V. Le. "[Learning to rank with nonsmooth cost functions.](http://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf)" Advances in neural information processing systems. 2007. 21 | - Zheng, Zhaohui, et al. "[A regression framework for learning ranking functions using relative relevance judgments.](https://www.cc.gatech.edu/~zha/papers/fp086-zheng.pdf)" Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007. 22 | - Qin, Tao, et al. "[Ranking with multiple hyperplanes.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.3093&rep=rep1&type=pdf)" Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007. 23 | 24 | ### 2008 25 | 26 | - Amini, Massih Reza, Tuong Vinh Truong, and Cyril Goutte. "[A boosting algorithm for learning bipartite ranking functions with partially labeled data.](http://ama.liglab.fr/~amini/Publis/SemiSupRanking_sigir08.pdf)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 27 | - Xu, Jun, et al. "[Directly optimizing evaluation measures in learning to rank.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.156.8455&rep=rep1&type=pdf)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 28 | - Veloso, Adriano A., et al. "[Learning to rank at query-time using association rules.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.5407&rep=rep1&type=pdf)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 29 | - Duh, Kevin, and Katrin Kirchhoff. "[Learning to rank with partially-labeled data.](http://www.cs.jhu.edu/~kevinduh/papers/duh08sigir.pdf)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 30 | - Guiver, John, and Edward Snelson. "[Learning to rank with softrank and gaussian processes.](https://dl.acm.org/doi/abs/10.1145/1390334.1390380)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 31 | - Zhou, Ke, et al. "[Learning to rank with ties.](https://dl.acm.org/doi/abs/10.1145/1390334.1390382)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 32 | - Geng, Xiubo, et al. "[Query dependent ranking using k-nearest neighbor.](https://andrewoarnold.com/fp025-geng.pdf)" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. 33 | 34 | ### 2009 35 | 36 | - Lease, Matthew. "[An improved markov random field model for supporting verbose queries.](https://www.ischool.utexas.edu/~ml/papers/lease-sigir09.pdf)" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009. 37 | - Aslam, Javed A., et al. "[Document selection methodologies for efficient and effective learning-to-rank.](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.2220&rep=rep1&type=pdf)" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009. 38 | - Donmez, Pinar, Krysta M. Svore, and Christopher JC Burges. "[On the local optimality of LambdaRank.](http://www.cs.cmu.edu/afs/.cs.cmu.edu/Web/People/pinard/Papers/sigirfp092-donmez.pdf)" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009. 39 | - Cummins, Ronan, and Colm O'Riordan. "[Learning in a pairwise term-term proximity framework for information retrieval.](https://www.researchgate.net/profile/Ronan_Cummins/publication/221299338_Learning_in_a_Pairwise_Term-Term_Proximity_Framework_for_Information_Retrieval/links/0912f50fa61d97a283000000.pdf)" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009. 40 | - Banerjee, Somnath, Soumen Chakrabarti, and Ganesh Ramakrishnan. "[Learning to rank for quantity consensus queries.](https://www.cse.iitb.ac.in/~soumen/doc/sigir2009q/QCQ.pdf)" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009. 41 | - Sun, Zhengya, et al. "[Robust sparse rank learning for non-smooth ranking measures.](https://www.researchgate.net/profile/Zhengya_Sun/publication/221301280_Robust_Sparse_Rank_Learning_for_Non-Smooth_Ranking_Measures/links/551bd2b20cf2909047b96a96.pdf)" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 2009. 42 | 43 | ### 2010 44 | 45 | - Burges, Christopher JC. "[From ranknet to lambdarank to lambdamart: An overview.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf)" Learning 11.23-581 (2010): 81. 46 | - Svore, Krysta M., Pallika H. Kanani, and Nazan Khan. "[How good is a span of terms? Exploiting proximity to improve web retrieval.](https://pallika.github.io/files/fp728-svore.pdf)" Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010. 47 | - Wang, Lidan, Jimmy Lin, and Donald Metzler. "[Learning to efficiently rank.](http://lintool.github.io/NSF-projects/IIS-1144034/publications/Wang_etal_SIGIR2010.pdf)" Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010. 48 | - Gao, Wei, et al. "[Learning to rank only using training data from related domain.](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5600&context=sis_research)" Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010. 49 | - Bagherjeiran, Abraham, Andrew O. Hatch, and Adwait Ratnaparkhi. "[Ranking for the conversion funnel.](https://dl.acm.org/doi/abs/10.1145/1835449.1835476)" Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010. 50 | 51 | ### 2011 52 | 53 | - Wang, Lidan, Jimmy Lin, and Donald Metzler. "[A cascade ranking model for efficient ranked retrieval.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.357.4790&rep=rep1&type=pdf)" Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011. 54 | - Dai, Na, Milad Shokouhi, and Brian D. Davison. "[Learning to rank for freshness and relevance.](https://www.microsoft.com/en-us/research/wp-content/uploads/2011/01/Dai2011.pdf)" Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011. 55 | - Ganjisaffar, Yasser, Rich Caruana, and Cristina Videira Lopes. "[Bagging gradient-boosted trees for high precision, low variance ranking models.](https://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/materials/bagging_lmbamart_jforests.pdf)" Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011. 56 | - Cai, Peng, et al. "[Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation.](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5597&context=sis_research)" Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011. 57 | - Chapelle, Olivier, and Yi Chang. "[Yahoo! learning to rank challenge overview.](http://proceedings.mlr.press/v14/chapelle11a/chapelle11a.pdf?WT.mc_id=Blog_MachLearn_General_DI)" Proceedings of the learning to rank challenge. 2011. 58 | 59 | ### 2012 60 | 61 | - Wang, Lidan, Paul N. Bennett, and Kevyn Collins-Thompson. "[Robust ranking models via risk-sensitive optimization.](http://www.cs.cmu.edu/afs/cs/Web/People/pbennett/papers/wang-et-al-sigir-2012.pdf)" Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 2012. 62 | - Severyn, Aliaksei, and Alessandro Moschitti. "[Structural relationships for large-scale learning of answer re-ranking.](http://dit.unitn.it/moschitti/articles/2012/SIGIR2012.pdf)" Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 2012. 63 | - Niu, Shuzi, et al. "[Top-k learning to rank: labeling, ranking and evaluation.](http://www.chinakdd.com/include/ueditor/jsp/upload/20120910/71891347254381370.pdf)" Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 2012. 64 | 65 | ### 2013 66 | 67 | - Paik, Jiaul H. "[A novel TF-IDF weighting scheme for effective ranking.](http://www.tyr.unlu.edu.ar/tallerIR/2014/papers/novel-tfidf.pdf)" Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 2013. 68 | - Wang, Hongning, et al. "[Personalized ranking model adaptation for web search.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.2388&rep=rep1&type=pdf)" Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 2013. 69 | - Raiber, Fiana, and Oren Kurland. "[Ranking document clusters using markov random fields.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.474.781&rep=rep1&type=pdf)" Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 2013. 70 | 71 | ### 2015 72 | 73 | - Grbovic, Mihajlo, et al. "[Context-and content-aware embeddings for query rewriting in sponsored search.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.707.9622&rep=rep1&type=pdf)" Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 2015. 74 | - Severyn, Aliaksei, and Alessandro Moschitti. "[Learning to rank short text pairs with convolutional deep neural networks.](http://eecs.csuohio.edu/~sschung/CIS660/RankShortTextCNNACM2015.pdf)" Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 2015. 75 | - Vulić, Ivan, and Marie-Francine Moens. "[Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings.](https://www2.kbs.uni-hannover.de/fileadmin/institut/pdf/webscience/2016-17/papers/got3.pdf)" Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 2015. 76 | 77 | ### 2016 78 | 79 | - Ustinovskiy, Yury, et al. "[An optimization framework for remapping and reweighting noisy relevance labels.](https://www.researchgate.net/profile/Pavel_Serdyukov/publication/305081472_An_Optimization_Framework_for_Remapping_and_Reweighting_Noisy_Relevance_Labels/links/5a9d3dc045851586a2aec23f/An-Optimization-Framework-for-Remapping-and-Reweighting-Noisy-Relevance-Labels.pdf)" Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 2016. 80 | - de Sá, Clebson CA, et al. "[Generalized BROOF-L2R: A general framework for learning to rank based on boosting and random forests.](https://dl.acm.org/doi/10.1145/2911451.2911540)" Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 2016. 81 | - Wang, Xuanhui, et al. "[Learning to rank with selection bias in personal search.](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45286.pdf)" Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 2016. 82 | 83 | ### 2017 84 | 85 | - Ibrahim, Muhammad, and Mark Carman. "[Comparing pointwise and listwise objective functions for random-forest-based learning-to-rank.](https://users.monash.edu.au/~mcarman/papers/ibrahim_TOIS2016_draft.pdf)" ACM Transactions on Information Systems (TOIS) 34.4 (2016): 1-38. 86 | - Chen, Ruey-Cheng, et al. "[Efficient cost-aware cascade ranking in multi-stage retrieval.](http://culpepper.io/publications/cgbc17-sigir.pdf)" Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017. 87 | - Xiong, Chenyan, et al. "[End-to-end neural ad-hoc ranking with kernel pooling.](https://arxiv.org/pdf/1706.06613.pdf)" Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval. 2017. [slide](https://pdfs.semanticscholar.org/ea73/8439b880ad033ff01602ea52d04b366d0d37.pdf) 88 | - Su, Yuxin, Irwin King, and Michael Lyu. "[Learning to rank using localized geometric mean metrics.](https://arxiv.org/pdf/1705.07563.pdf)" Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017. [slide](https://www.slideshare.net/YuxinSu/sigir17-learning-to-rank-using-localized-geometric-mean-metrics) 89 | - Dehghani, Mostafa, et al. "[Neural ranking models with weak supervision.](https://arxiv.org/pdf/1704.08803.pdf)" Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017. [slide](https://mostafadehghani.com/wp-content/uploads/2016/07/SIGIR2017_Presentation.pdf) 90 | - Karmaker Santu, Shubhra Kanti, Parikshit Sondhi, and ChengXiang Zhai. "[On application of learning to rank for e-commerce search.](https://arxiv.org/pdf/1903.04263.pdf)" Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017. 91 | 92 | ### 2018 93 | 94 | - He, Xiangnan, et al. "[Adversarial personalized ranking for recommendation.](https://arxiv.org/pdf/1808.03908.pdf)" The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018. [code](https://github.com/hexiangnan/adversarial_personalized_ranking) 95 | - Wang, Huazheng, et al. "[Efficient exploration of gradient space for online learning to rank.](https://arxiv.org/pdf/1805.07317.pdf)" The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018. 96 | - Dato, Domenico, et al. "[Fast ranking with additive ensembles of oblivious and non-oblivious regression trees.](https://iris.unive.it/retrieve/handle/10278/3692219/191752/paper.pdf)" ACM Transactions on Information Systems (TOIS) 35.2 (2016): 1-31. [slide](https://www.slideshare.net/raffaeleperego/quickscorer-a-fast-algorithm-to-rank-documents-with-additive-ensembles-of-regression-trees) 97 | - Feng, Yue, et al. "[From greedy selection to exploratory decision-making: Diverse ranking with policy-value networks.](http://159.226.40.238/~junxu/publications/SIGIR2018-M2Div.pdf)" The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018. 98 | - Ai, Qingyao, et al. "[Learning a deep listwise context model for ranking refinement.](https://arxiv.org/pdf/1804.05936.pdf)" The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018. [code](https://github.com/QingyaoAi/Deep-Listwise-Context-Model-for-Ranking-Refinement) 99 | - Fan, Yixing, et al. "[Modeling diverse relevance patterns in ad-hoc retrieval.](https://arxiv.org/pdf/1805.05737.pdf)" The 41st international ACM SIGIR conference on research & development in information retrieval. 2018. [code](https://github.com/faneshion/HiNT) 100 | - Lucchese, Claudio, et al. "[Selective gradient boosting for effective learning to rank.](https://arca.unive.it/retrieve/handle/10278/3703677/191780/selective-SIGIR2018.pdf)" The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018. 101 | - Wu, Liang, et al. "[Turning clicks into purchases: Revenue optimization for product search in e-commerce.](http://www.liangwu.me/files/turning-clicks-purchases.pdf)" The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018. 102 | 103 | ### 2019 104 | 105 | - Pasumarthi, Rama Kumar, et al. "[Tf-ranking: Scalable tensorflow library for learning-to-rank.](https://arxiv.org/pdf/1812.00073.pdf)" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. 106 | 107 | ### 2020 108 | 109 | - Qu, Chen, et al. "[Contextual Re-Ranking with Behavior Aware Transformers.](http://ciir-publications.cs.umass.edu/getpdf.php?id=1383)" Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. 110 | - MacAvaney, Sean, et al. "[Efficient Document Re-Ranking for Transformers by Precomputing Term Representations.](https://arxiv.org/pdf/2004.14255.pdf)" The 43st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2020. [code](https://github.com/Georgetown-IR-Lab/prettr-neural-ir)? 111 | - Zhuang, Honglei, et al. "[Feature transformation for neural ranking models.](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/03d9dbc56c3d1b19a611043a4cb72e227ebba249.pdf)" Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. 112 | - Lucchese, Claudio, et al. "[Query-level Early Exit for Additive Learning-to-Rank Ensembles.](https://arxiv.org/pdf/2004.14641.pdf)" The 43st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2020. 113 | - Bevendorff, Maik Fröbe1 Janek, et al. "[Sampling Bias Due to Near-Duplicates in Learning to Rank.](https://webis.de/downloads/publications/papers/webis_2020d.pdf)" The 43st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2020. [code](https://github.com/webis-de/SIGIR-20) 114 | 115 | ## Software 116 | 117 | - [Support Vector Machine for Ranking](http://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html) 118 | - [Support Vector Machine for Optimizing Mean Average Precision](http://projects.yisongyue.com/svmmap/) 119 | - [TensorFlow Ranking](https://github.com/tensorflow/ranking) 120 | - [LambdaRank Example on LightGBM](https://github.com/Microsoft/LightGBM/tree/master/examples/lambdarank) 121 | - [Chainer implementation of RankNet](https://github.com/kzkadc/ranknet) 122 | - [jforests](https://github.com/yasserg/jforests) 123 | - [ListNet](https://sourceforge.net/projects/listnet/) 124 | - [ListMLE](https://sourceforge.net/projects/listmle/) 125 | - [Metric Learning to Rank](https://github.com/bmcfee/mlr) 126 | - [Lerot](https://bitbucket.org/ilps/lerot/src/master/) 127 | - [xapian-letor](https://github.com/xapian/xapian/tree/master/xapian-letor) 128 | - [OpenNIR](https://opennir.net/) 129 | - [metarank](https://www.metarank.ai/) 130 | 131 | ## Dataset 132 | 133 | - [LETOR 3.0/4.0](https://www.microsoft.com/en-us/research/project/letor-learning-rank-information-retrieval/) 134 | - [MSLR WEB10K/WEB30K](https://www.microsoft.com/en-us/research/project/mslr/) 135 | - [TREC QA Track Data](https://trec.nist.gov/data/qamain.html) 136 | 137 | ## Others 138 | 139 | - [QuickRank](https://github.com/hpclab/quickrank) 140 | - [ExpediaLearningToRank](https://github.com/arifqodari/ExpediaLearningToRank) 141 | - [ランク学習（Learning to Rank） Advent Calendar 2018](https://adventar.org/calendars/3357) 142 | - [DSIRNLP#1 ランキング学習ことはじめ](https://www.slideshare.net/sleepy_yoshi/dsirnlp1) 143 | - [Learning to rank (LTR) とは何か](https://qiita.com/sugiyamath/items/ba08874490e21a9a3ac1) 144 | - [SIGIR2011読み会 3: Learning to Rank](https://www.slideshare.net/sleepy_yoshi/sigir2011-3-learning-to-rank) 145 | - [SIGIR2012勉強会 23: Learning to Rank](https://www.slideshare.net/sleepy_yoshi/sigir2012-23-learning-to-rank) 146 | 147 | --------------------------------------------------------------------------------