@article{Rougier:2018,
  doi = {10.1038/d41586-018-04628-w},
  year = {2018},
  month = 04,
  publisher = {Springer Nature},
  volume = {556},
  number = {7701},
  pages = {309--309},
  author = {Rougier, Nicolas P. and Hinsen, Konrad},
  title = {Code reviewing puts extra demands on referees },
  journal = {Nature},
}

@article{Science:2018,
  doi = {10.1126/science.359.6377.725},
  author = {Hutson, Matthew},
  url = {http://science.sciencemag.org/content/359/6377/725},
  year = {2018},
  month = 02,
  volume = {359},
  number = {6377},
  title = {Artificial intelligence faces a replication crisis},
  journal = {Science},
}


@Misc{Roboto:2011,
  author =       {Christian Robertson},
  title =        {The Roboto family of fonts (Google)},
  url =          {https://github.com/google/roboto},
  year =         2011,
  note =         {Apache License, verison 2.0},
}

@Misc{SourceSerifPro:2014,
  author =       {Frank Grießhammer},
  title =        {Source Serif Pro (Adobe Systems)},
  url =          {https://github.com/adobe-fonts/source-serif-pro},
  year =         2014,
  note =         {SIL Open Font License, version 1.1},
}

@Misc{SourceCodePro:2012,
  author =       {Paul D. Hunt},
  title =        {Source Code Pro (Adobe Systems)},
  url =          {https://github.com/adobe-fonts/source-code-pro},
  year =         2012,
  note =         {SIL Open Font License, version 1.1},
}


@article{Topalidou:2015,
  author =       {Topalidou, Meropi and Rougier, Nicolas P.},
  title =        {{[Re] Interaction between cognitive and motor cortico-basal
                  ganglia loops during decision making: a computational study}},
  journal =      {ReScience},
  year =         2015,
  volume =       1,
  number =       1,
  doi =          {10.5281/zenodo.27944},
}

@article{Rougier:2017,
  doi =          {10.7717/peerj-cs.142},
  author =       {Nicolas P. Rougier and Konrad Hinsen and Frédéric Alexandre
                  and Thomas Arildsen and Lorena Barba and Fabien
                  C. Y. Benureau and C. Titus Brown and Pierre de Buyl and Ozan
                  Caglayan and Andrew P. Davison and Marc André Delsuc and
                  Georgios Detorakis and Alexandra K. Diem and Damien Drix and
                  Pierre Enel and Benoît Girard and Olivia Guest and Matt
                  G. Hall and Rafael Neto Henriques and Xavier Hinaut and Kamil
                  S Jaron and Mehdi Khamassi and Almar Klein and Tiina Manninen
                  and Pietro Marchesi and Dan McGlinn and Christoph Metzner and
                  Owen L. Petchey and Hans Ekkehard Plesser and Timothée Poisot
                  and Karthik Ram and Yoav Ram and Etienne Roesch and Cyrille
                  Rossant and Vahid Rostami and Aaron Shifman and Joseph
                  Stachelek and Marcel Stimberg and Frank Stollmeier and
                  Federico Vaggi and Guillaume Viejo and Julien Vitay and Anya
                  Vostinar and Roman Yurchak and Tiziano Zito},
  title =        {{Sustainable computational science: the ReScience
                  initiative}},
  journal =      {{PeerJ} Computer Science},
  month =        12,
  volume =       3,
  pages =        {e142},
  year =         2017,
  github =       {https://github.com/ReScience/ReScience-article-2},
  keywords =     {journal},
  tags =         {OS},
}

@article{Sinha:2022,
  author = {Sinha, Koustuv and Dodge, Jesse and Luccioni, Sasha and Forde, Jessica Zosa and Raparthy, Sharath Chandra and Pineau, Joelle and Stojnic, Robert},
  title = {{ML Reproducibility Challenge 2021}},
  journal = {ReScience C},
  year = {2022},
  month = 03,
  volume = {8},
  number = {2},
  pages = {{#48}},
  doi = {10.5281/zenodo.6574723},
  url = {https://zenodo.org/record/6574723/files/article.pdf},
  code_url = {},
  code_doi = {},
  code_swh = {},
  data_url = {},
  data_doi = {},
  review_url = {},
  type = {Editorial},
  language = {Python},
  domain = {ML Reproducibility Challenge 2021},
  keywords = {rescience c, machine learning, deep learning, python, pytorch}
}

@article{sweeney2013,
  author    = {Latanya Sweeney},
  title     = {Discrimination in Online Ad Delivery},
  journal   = {CoRR},
  volume    = {abs/1301.6822},
  year      = {2013},
  url       = {http://arxiv.org/abs/1301.6822},
  eprinttype = {arXiv},
  eprint    = {1301.6822},
  timestamp = {Mon, 13 Aug 2018 16:46:31 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1301-6822.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Sinha:2021,
  author = {Sinha, Koustuv and Dodge, Jesse and Luccioni, Sasha and Forde, Jessica Zosa and Stojnic, Robert and Pineau, Joelle},
  title = {{ML Reproducibility Challenge 2020}},
  journal = {ReScience C},
  year = {2021},
  month = 03,
  volume = {7},
  number = {2},
  pages = {{#1}},
  doi = {10.5281/zenodo.4833117},
  url = {https://zenodo.org/record/4833117/files/article.pdf},
  code_url = {},
  code_doi = {},
  code_swh = {},
  data_url = {},
  data_doi = {},
  review_url = {},
  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, neurips, reproducibility challenge}
}

@article{Sinha:2020,
  author = {Sinha, Koustuv and Pineau, Joelle and Forde, Jessica and Ke, Rosemary Nan and Larochelle, Hugo},
  title = {{NeurIPS 2019 Reproducibility Challenge}},
  journal = {ReScience C},
  year = {2020},
  month = 05,
  volume = {6},
  number = {2},
  pages = {{#11}},
  doi = {10.5281/zenodo.3818627},
  url = {https://zenodo.org/record/3818627/files/article.pdf},
  code_url = {},
  code_doi = {},
  code_swh = {},
  data_url = {},
  data_doi = {},
  review_url = {},
  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, neurips, reproducibility challenge}
}

@article{Pineau:2019,
  author = {Pineau, Joelle and Sinha, Koustuv and Fried, Genevieve and Ke, Rosemary Nan and Larochelle, Hugo},
  title = {{ICLR Reproducibility Challenge 2019}},
  journal = {ReScience C},
  year = {2019},
  month = 05,
  volume = {5},
  number = {2},
  pages = {{#5}},
  doi = {10.5281/zenodo.3158244},
  url = {https://zenodo.org/record/3158244/files/article.pdf},
  code_url = {},
  code_doi = {},
  data_url = {},
  data_doi = {},
  review_url = {https://github.com/ReScience/submissions/issues/5},
  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, ICLR, reproducibility challenge}
}

@article{Onal2018NeuralIR,
  title={Neural information retrieval: at the end of the early years},
  author={Kezban Dilek Onal and Ye Zhang and Ismail Seng{\"o}r Alting{\"o}vde and Md. Mustafizur Rahman and Pinar Senkul and Alexander Braylan and Brandon Dang and Heng-Lu Chang and Henna Kim and Quinten McNamara and Aaron Angert and Edward Banner and Vivek Khetan and Tyler McDonnell and An Thanh Nguyen and Dan Xu and Byron C. Wallace and M. de Rijke and Matthew Lease},
  journal={Information Retrieval Journal},
  year={2018},
  volume={21},
  pages={111-182}
}

@article{kenter2018,
  author    = {Tom Kenter and
               Alexey Borisov and
               Christophe Van Gysel and
               Mostafa Dehghani and
               Maarten de Rijke and
               Bhaskar Mitra},
  title     = {Neural Networks for Information Retrieval},
  journal   = {CoRR},
  volume    = {abs/1801.02178},
  year      = {2018},
  url       = {http://arxiv.org/abs/1801.02178},
  eprinttype = {arXiv},
  eprint    = {1801.02178},
  timestamp = {Thu, 28 Apr 2022 12:21:10 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1801-02178.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{mitra2017neural,
author = {Mitra, Bhaskar and Craswell, Nick},
title = {Neural Models for Information Retrieval},
year = {2017},
month = {05},
abstract = {Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ machine learning techniques over hand-crafted IR features. By contrast, neural models learn representations of language from raw text that can bridge the gap between query and document vocabulary. Unlike classical IR models, these new machine learning based approaches are data-hungry, requiring large scale training data before they can be deployed. This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of traditional retrieval models. We begin by introducing fundamental concepts of IR and different neural and non-neural approaches to learning vector representations of text. We then review shallow neural IR methods that employ pre-trained neural term embeddings without learning the IR task end-to-end. We introduce deep neural networks next, discussing popular deep architectures. Finally, we review the current DNN models for information retrieval. We conclude with a discussion on potential future directions for neural IR.},
publisher = {arXiv preprint arXiv:1705.01509},
url = {https://www.microsoft.com/en-us/research/publication/neural-models-information-retrieval/},
}

@misc{Wugit2022,
  author = {H. Wu},
  title = {JMEFairness},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/haolun-wu/jmefairness}},
  commit = {0573f5b39f00e021c5c2e1cdce19f0731527e47a}
}
@software{latataro,
  author = {username: latataro},
  month = {01},
  title = {{BPRMF}},
  url = {https://github.com/jchanxtarov/bprmf},
  version = {2.0.4},
  year = {2022}
}
@misc{Sungit2019,
  author = {F. Sun},
  title = {TorchRec Bert4Rec},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/pytorch/torchrec/tree/main/examples/bert4rec}},
  commit = {d9cc3e0157b55134be65f9aea85d3215f341b385}
}




@Conference{Crawford:2017,
  author = "Kate Crawford",
  title  = "The trouble with bias - Conference on Neural Information Processing Systems, invited speaker",
  year   = "2017"}

@article{Sun:2019,
  author    = {Fei Sun and
               Jun Liu and
               Jian Wu and
               Changhua Pei and
               Xiao Lin and
               Wenwu Ou and
               Peng Jiang},
  title     = {BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer},
  journal   = {CoRR},
  volume    = {abs/1904.06690},
  year      = {2019},
  url       = {http://arxiv.org/abs/1904.06690},
  eprinttype = {arXiv},
  eprint    = {1904.06690},
  timestamp = {Thu, 18 Nov 2021 09:19:54 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1904-06690.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}




@article{kay,
author = {Kay, Matthew and Matuszek, Cynthia and Munson, Sean},
year = {2015},
month = {04},
pages = {},
title = {Unequal Representation and Gender Stereotypes in Image Search Results for Occupations},
doi = {10.1145/2702123.2702520}
}

@article{joachims_2017,
  author    = {Thorsten Joachims and
               Adith Swaminathan and
               Tobias Schnabel},
  title     = {Unbiased Learning-to-Rank with Biased Feedback},
  journal   = {CoRR},
  volume    = {abs/1608.04468},
  year      = {2016},
  url       = {http://arxiv.org/abs/1608.04468},
  eprinttype = {arXiv},
  eprint    = {1608.04468},
  timestamp = {Mon, 13 Aug 2018 16:49:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/JoachimsSS16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{joachims_2018,
author = {Joachims, Thorsten and Swaminathan, Adith and Schnabel, Tobias},
title = {Unbiased Learning-to-Rank with Biased Feedback},
year = {2018},
isbn = {9780999241127},
publisher = {AAAI Press},
abstract = {Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user-centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a propensity-weighted ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model mis-specification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.},
booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence},
pages = {5284–5288},
numpages = {5},
location = {Stockholm, Sweden},
series = {IJCAI'18}
}

@inproceedings{Radlinski2008,
author = {Radlinski, Filip and Kleinberg, Robert and Joachims, Thorsten},
title = {Learning Diverse Rankings with Multi-Armed Bandits},
year = {2008},
isbn = {9781605582054},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390156.1390255},
doi = {10.1145/1390156.1390255},
booktitle = {Proceedings of the 25th International Conference on Machine Learning},
pages = {784–791},
numpages = {8},
location = {Helsinki, Finland},
series = {ICML '08}
}



@inproceedings{Radlinski2007,
author = {Radlinski, Filip and Joachims, Thorsten},
title = {Active Exploration for Learning Rankings from Clickthrough Data},
year = {2007},
isbn = {9781595936097},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1281192.1281254},
doi = {10.1145/1281192.1281254},
booktitle = {Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {570–579},
numpages = {10},
keywords = {learning to rank, clickthrough data, web search, active exploration},
location = {San Jose, California, USA},
series = {KDD '07}
}

@article{Blei2003,
author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
title = {Latent Dirichlet Allocation},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.},
journal = {J. Mach. Learn. Res.},
month = {03},
pages = {993–1022},
numpages = {30}
}

@inproceedings{Valcarce2018,
author = {Valcarce, Daniel and Bellog\'{\i}n, Alejandro and Parapar, Javier and Castells, Pablo},
title = {On the Robustness and Discriminative Power of Information Retrieval Metrics for Top-N Recommendation},
year = {2018},
isbn = {9781450359016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3240323.3240347},
doi = {10.1145/3240323.3240347},
booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems},
pages = {260–268},
numpages = {9},
location = {Vancouver, British Columbia, Canada},
series = {RecSys '18}
}

@inproceedings{Oosterhuis2020,
author = {Oosterhuis, Harrie and de Rijke, Maarten},
title = {Policy-Aware Unbiased Learning to Rank for Top-k Rankings},
year = {2020},
isbn = {9781450380164},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3397271.3401102},
doi = {10.1145/3397271.3401102},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {489–498},
numpages = {10},
keywords = {learning to rank, recommendation, selection bias, top-k ranking, counterfactual learning, counterfactual learning to rank},
location = {Virtual Event, China},
series = {SIGIR '20}
}

@article{pandey2005,
  author    = {Sandeep Pandey and
               Sourashis Roy and
               Christopher Olston and
               Junghoo Cho and
               Soumen Chakrabarti},
  title     = {Shuffling a Stacked Deck: The Case for Partially Randomized Ranking
               of Search Engine Results},
  journal   = {CoRR},
  volume    = {abs/cs/0503011},
  year      = {2005},
  url       = {http://arxiv.org/abs/cs/0503011},
  eprinttype = {arXiv},
  eprint    = {cs/0503011},
  timestamp = {Mon, 13 Aug 2018 16:48:24 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-cs-0503011.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Yaday2019,
  author    = {Himank Yadav and
               Zhengxiao Du and
               Thorsten Joachims},
  title     = {Fair Learning-to-Rank from Implicit Feedback},
  journal   = {CoRR},
  volume    = {abs/1911.08054},
  year      = {2019},
  url       = {http://arxiv.org/abs/1911.08054},
  eprinttype = {arXiv},
  eprint    = {1911.08054},
  timestamp = {Mon, 02 Dec 2019 17:48:37 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1911-08054.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Singh_2018,
	doi = {10.1145/3219819.3220088},
  
	url = {https://doi.org/10.1145\%2F3219819.3220088},
  
	year = 2018,
	month = {6},
  
	publisher = {{ACM}},
  
	author = {Ashudeep Singh and Thorsten Joachims},
  
	title = {Fairness of Exposure in Rankings},
  
	booktitle = {Proceedings of the 24th {ACM} {SIGKDD} International Conference on Knowledge Discovery {\&}amp$\mathsemicolon$ Data Mining}
}


@article{singh2019,
  author    = {Ashudeep Singh and
               Thorsten Joachims},
  title     = {Policy Learning for Fairness in Ranking},
  journal   = {CoRR},
  volume    = {abs/1902.04056},
  year      = {2019},
  url       = {http://arxiv.org/abs/1902.04056},
  eprinttype = {arXiv},
  eprint    = {1902.04056},
  timestamp = {Tue, 21 May 2019 18:03:40 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1902-04056.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
  
@article{DBLP:Diaz,
  author    = {Fernando Diaz and
               Bhaskar Mitra and
               Michael D. Ekstrand and
               Asia J. Biega and
               Ben Carterette},
  title     = {Evaluating Stochastic Rankings with Expected Exposure},
  journal   = {CoRR},
  volume    = {abs/2004.13157},
  year      = {2020},
  url       = {https://arxiv.org/abs/2004.13157},
  eprinttype = {arXiv},
  eprint    = {2004.13157},
  timestamp = {Wed, 27 Apr 2022 14:24:37 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2004-13157.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/Burke17aa,
  author    = {Robin Burke},
  title     = {Multisided Fairness for Recommendation},
  journal   = {CoRR},
  volume    = {abs/1707.00093},
  year      = {2017},
  url       = {http://arxiv.org/abs/1707.00093},
  eprinttype = {arXiv},
  eprint    = {1707.00093},
  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/Burke17aa.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{TieYan2009,
author = {Liu, Tie-Yan},
title = {Learning to Rank for Information Retrieval},
year = {2009},
issue_date = {March 2009},
publisher = {Now Publishers Inc.},
address = {Hanover, MA, USA},
volume = {3},
number = {3},
issn = {1554-0669},
url = {https://doi.org/10.1561/1500000016},
doi = {10.1561/1500000016},
abstract = {Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.},
journal = {Found. Trends Inf. Retr.},
month = {3},
pages = {225–331},
numpages = {107}
}

@article{Moffat2008,
author = {Moffat, Alistair and Zobel, Justin},
title = {Rank-Biased Precision for Measurement of Retrieval Effectiveness},
year = {2008},
issue_date = {December 2008},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {27},
number = {1},
issn = {1046-8188},
url = {https://doi.org/10.1145/1416950.1416952},
doi = {10.1145/1416950.1416952},
abstract = {A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings. For example, recall is not well founded as a measure of satisfaction, since the user of an actual system cannot judge recall. Average precision is derived from recall, and suffers from the same problem. In addition, average precision lacks key stability properties that are needed for robust experiments. In this article, we introduce a new effectiveness metric, rank-biased precision, that avoids these problems. Rank-biased pre-cision is derived from a simple model of user behavior, is robust if answer rankings are extended to greater depths, and allows accurate quantification of experimental uncertainty, even when only partial relevance judgments are available.},
journal = {ACM Trans. Inf. Syst.},
month = {12},
articleno = {2},
numpages = {27},
keywords = {precision, relevance, Recall, average precision, pooling}
}

@article{DBLP:journals/corr/abs-2105-05779,
  author    = {Michael D. Ekstrand and
               Anubrata Das and
               Robin Burke and
               Fernando Diaz},
  title     = {Fairness and Discrimination in Information Access Systems},
  journal   = {CoRR},
  volume    = {abs/2105.05779},
  year      = {2021},
  url       = {https://arxiv.org/abs/2105.05779},
  eprinttype = {arXiv},
  eprint    = {2105.05779},
  timestamp = {Thu, 23 Jun 2022 20:00:35 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-05779.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{wu,
  doi = {10.48550/ARXIV.2205.00048},
  
  url = {https://arxiv.org/abs/2205.00048},
  
  author = {Wu, Haolun and Mitra, Bhaskar and Ma, Chen and Diaz, Fernando and Liu, Xue},
  
  keywords = {Information Retrieval (cs.IR), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Joint Multisided Exposure Fairness for Recommendation},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{kendall_rank, 
title={Rank Correlation Methods.},
    author={Maurice George Kendall}, 
    year={1949}
}

@book{book,
author = {Liu, Tie-Yan},
year = {2007},
month = {01},
pages = {},
title = {Learning to Rank for Information Retrieval},
isbn = {9781601982452},
doi = {10.1561/9781601982452}
}

@article{movielens1m,
author = {Harper, F. Maxwell and Konstan, Joseph A.},
title = {The MovieLens Datasets: History and Context},
year = {2015},
issue_date = {January 2016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {5},
number = {4},
issn = {2160-6455},
url = {https://doi.org/10.1145/2827872},
doi = {10.1145/2827872},
abstract = {The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.},
journal = {ACM Trans. Interact. Intell. Syst.},
month = {12},
articleno = {19},
numpages = {19},
keywords = {MovieLens, ratings, Datasets, recommendations}
}

@Book{Luce59,
  Title                    = {Individual Choice Behavior: A Theoretical analysis},
  Author                   = {R. Duncan Luce},
  Publisher                = {Wiley},
  Year                     = {1959},

  Address                  = {New York, NY, USA}
}

@article{plackett,
 ISSN = {00359254, 14679876},
 URL = {http://www.jstor.org/stable/2346567},
 abstract = {A probability distribution is defined over the r! permutations of r objects in such a way as to incorporate up to r! - 1 parameters. Problems of estimation and testing are considered. The results are applied to data on voting at elections and beanstores.},
 author = {R. L. Plackett},
 journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
 number = {2},
 pages = {193--202},
 publisher = {[Wiley, Royal Statistical Society]},
 title = {The Analysis of Permutations},
 urldate = {2023-01-26},
 volume = {24},
 year = {1975}
}

@article{Luce1977TheCA,
  title={The Choice Axiom after Twenty Years},
  author={R. Duncan Luce},
  journal={Journal of Mathematical Psychology},
  year={1977},
  volume={15},
  pages={215-233}
}

@inproceedings{inproceedings,
author = {Cai, Chenwei and He, Ruining and McAuley, Julian},
year = {2017},
month = {08},
pages = {1476-1482},
title = {SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation},
doi = {10.24963/ijcai.2017/204}
}

@inproceedings{inproceedings2,
author = {Zhao, Tong and McAuley, Julian and King, Irwin},
year = {2015},
month = {10},
pages = {821-830},
title = {Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation},
doi = {10.1145/2806416.2806511}
}
