%% publication record

@article{yu2022towards,
  title={Towards Fairness-Aware Multi-Objective Optimization},
  author={Yu, Guo and Ma, Lianbo and Du, Wei and Du, Wenli and Jin, Yaochu},
  journal={arXiv preprint arXiv:2207.12138},
  year={2022}
}

@inproceedings{lyu2020deep,
  title={Deep match to rank model for personalized click-through rate prediction},
  author={Lyu, Ze and Dong, Yu and Huo, Chengfu and Ren, Weijun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={01},
  pages={156--163},
  year={2020}
}
@inproceedings{revaud2019learning,
  title={Learning with average precision: Training image retrieval with a listwise loss},
  author={Revaud, Jerome and Almazan, Jon and Rezende, Rafael S and Souza, Cesar Roberto de},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5107--5116},
  year={2019}
}
@inproceedings{bruch2019analysis,
  title={An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance},
  author={Bruch, Sebastian and Wang, Xuanhui and Bendersky, Michael and Najork, Marc},
  booktitle={Proceedings of the 2019 ACM SIGIR international conference on theory of information retrieval},
  pages={75--78},
  year={2019}
}
@inproceedings{burges2005learning,
  title={Learning to rank using gradient descent},
  author={Burges, Chris and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Greg},
  booktitle={Proceedings of the 22nd international conference on Machine learning},
  pages={89--96},
  year={2005}
}


@article{Jrvelin2002CumulatedGE,
  title={Cumulated gain-based evaluation of IR techniques},
  author={Kalervo J{\"a}rvelin and Jaana Kek{\"a}l{\"a}inen},
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@inproceedings{NIPS2006_af44c4c5,
 author = {Burges, Christopher and Ragno, Robert and Le, Quoc},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
 pages = {},
 publisher = {MIT Press},
 title = {Learning to Rank with Nonsmooth Cost Functions},
 url = {https://proceedings.neurips.cc/paper_files/paper/2006/file/af44c4c56f385c43f2529f9b1b018f6a-Paper.pdf},
 volume = {19},
 year = {2006}
}

@article{yan2007,
author = {Joachims, Thorsten and Li, Hang and Liu, Tie-Yan and Zhai, ChengXiang},
year = {2007},
month = {12},
pages = {58-62},
title = {Learning to rank for information retrieval (LR4IR 2007)},
volume = {41},
journal = {SIGIR Forum},
doi = {10.1145/1328964.1328974}
}
@inproceedings{yu2019wassrank,
  title={WassRank: Listwise document ranking using optimal transport theory},
  author={Yu, Hai-Tao and Jatowt, Adam and Joho, Hideo and Jose, Joemon M and Yang, Xiao and Chen, Long},
  booktitle={Proceedings of the twelfth ACM international conference on web search and data mining},
  pages={24--32},
  year={2019}
}


@article{jing2019deep,
  title={A deep survival analysis method based on ranking},
  author={Jing, Bingzhong and Zhang, Tao and Wang, Zixian and Jin, Ying and Liu, Kuiyuan and Qiu, Wenze and Ke, Liangru and Sun, Ying and He, Caisheng and Hou, Dan and others},
  journal={Artificial intelligence in medicine},
  volume={98},
  pages={1--9},
  year={2019},
  publisher={Elsevier}
}
@article{wang2021pairwise,
  title={Pairwise learning for neural link prediction},
  author={Wang, Zhitao and Zhou, Yong and Hong, Litao and Zou, Yuanhang and Su, Hanjing and Chen, Shouzhi},
  journal={arXiv preprint arXiv:2112.02936},
  year={2021}
}
@inproceedings{kumar2020deep,
  title={Deep attentive ranking networks for learning to order sentences},
  author={Kumar, Pawan and Brahma, Dhanajit and Karnick, Harish and Rai, Piyush},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={05},
  pages={8115--8122},
  year={2020}
}
@inproceedings{xiao2020deep,
  title={Deep multi-interest network for click-through rate prediction},
  author={Xiao, Zhibo and Yang, Luwei and Jiang, Wen and Wei, Yi and Hu, Yi and Wang, Hao},
  booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
  pages={2265--2268},
  year={2020}
}
@inproceedings{yang2020empirically,
  title={Empirically testing deep and shallow ranking models for click-through rate (CTR) prediction},
  author={Yang, Yi-Che and Lai, Ping-Ching and Chen, Hung-Hsuan},
  booktitle={2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)},
  pages={147--152},
  year={2020},
  organization={IEEE}
}

@techreport{burges2010ranknet,
  added-at = {2013-01-12T16:27:53.000+0100},
  author = {Burges, Christopher J. C.},
  biburl = {https://www.bibsonomy.org/bibtex/2890499272759d00d48b45aa8826018fb/nosebrain},
  institution = {Microsoft Research},
  interhash = {6fc638e9013fd05627d96645690214a4},
  intrahash = {890499272759d00d48b45aa8826018fb},
  keywords = {LambdaMART LambdaRank RankNet learning ranking},
  timestamp = {2013-01-12T16:27:53.000+0100},
  title = {From {RankNet} to {LambdaRank} to {LambdaMART}: An Overview},
  url = {http://research.microsoft.com/en-us/um/people/cburges/tech\_reports/MSR-TR-2010-82.pdf},
  year = 2010
}

@article{li2007mcrank,
  title={Mcrank: Learning to rank using multiple classification and gradient boosting},
  author={Li, Ping and Wu, Qiang and Burges, Christopher},
  journal={Advances in neural information processing systems},
  volume={20},
  year={2007}
}
@ARTICLE{4655444,
  author={Cossock, David and Zhang, Tong},
  journal={IEEE Transactions on Information Theory}, 
  title={Statistical Analysis of Bayes Optimal Subset Ranking}, 
  year={2008},
  volume={54},
  number={11},
  pages={5140-5154},
  doi={10.1109/TIT.2008.929939}}
@inproceedings{zhang2021fairer,
  title={Fairer machine learning through multi-objective evolutionary learning},
  author={Zhang, Qingquan and Liu, Jialin and Zhang, Zeqi and Wen, Junyi and Mao, Bifei and Yao, Xin},
  booktitle={Artificial Neural Networks and Machine Learning--ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14--17, 2021, Proceedings, Part IV},
  pages={111--123},
  year={2021},
  organization={Springer}
}
@article{yu2022survey,
  title={A Survey on Knee-Oriented Multiobjective Evolutionary Optimization},
  author={Yu, Guo and Ma, Lianbo and Jin, Yaochu and Du, Wenli and Liu, Qiqi and Zhang, Hengmin},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={26},
  number={6},
  pages={1452--1472},
  year={2022},
  publisher={IEEE}
}

@article{geoff2017on,
  title={On Fairness and Calibration},
  author={Geoff, Pleiss and Manish, Raghavan and Felix, Wu and Jon, Kleinberg and Kilian Q. Weinberger},
  journal={Advances in neural information processing systems},
  year={2017}
}
@article{tian2019fair,
  title={A fair approach for multi-objective water resources allocation},
  author={Tian, Jing and Guo, Shenglian and Liu, Dedi and Pan, Zhengke and Hong, Xingjun},
  journal={Water Resources Management},
  volume={33},
  pages={3633--3653},
  year={2019},
  publisher={Springer}
}
@article{chouldechova2017fair,
  title={Fair prediction with disparate impact: A study of bias in recidivism prediction instruments},
  author={Chouldechova, Alexandra},
  journal={Big data},
  volume={5},
  number={2},
  pages={153--163},
  year={2017},
  publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}

@article{naumov2019deep,
  title={Deep learning recommendation model for personalization and recommendation systems},
  author={Naumov, Maxim and Mudigere, Dheevatsa and Shi, Hao-Jun Michael and Huang, Jianyu and Sundaraman, Narayanan and Park, Jongsoo and Wang, Xiaodong and Gupta, Udit and Wu, Carole-Jean and Azzolini, Alisson G and others},
  journal={arXiv preprint arXiv:1906.00091},
  year={2019}
}

@article{wu2022multi,
  title={A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation},
  author={Wu, Haolun and Ma, Chen and Mitra, Bhaskar and Diaz, Fernando and Liu, Xue},
  journal={ACM Transactions on Information Systems},
  volume={41},
  number={2},
  pages={1--29},
  year={2022},
  publisher={ACM New York, NY}
}

@article{chen2019diversity,
  title={A diversity ranking based evolutionary algorithm for multi-objective and many-objective optimization},
  author={Chen, Guoyu and Li, Junhua},
  journal={Swarm and Evolutionary Computation},
  volume={48},
  pages={274--287},
  year={2019},
  publisher={Elsevier}
}