Fetching the paper…
Reading the bibliography…
Numerous works propose post-hoc, model-agnostic explanations for learning to rank, focusing on ordering entities by their relevance to a query through feature attribution methods.
A new measure of rank correlation
Maurice G Kendall · 1938
Earlier work this paper cites.
A value for n-person games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Monotonic solutions of cooperative games
Hobart P. Young · 1985
Earlier work this paper cites.
Simple, proven approaches to text retrieval
Stephen E Robertson and Karen Spärck Jones · 1994
Earlier work this paper cites.
Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
Earlier work this paper cites.
Overview of the trec 2003 robust retrieval track
Ellen M Voorhees et al · 2003
Earlier work this paper cites.
A formal study of information retrieval heuristics
Hui Fang, Tao Tao, and ChengXiang Zhai · 2004
Earlier work this paper cites.
A decision theoretic framework for ranking using implicit feedback
Onno Zoeter, Mike Taylor, Ed Snelson, John Guiver, Nick Craswell, and Martin Szummer · 2008
Earlier work this paper cites.
A general approximation framework for direct optimization of information retrieval measures
Tao Qin, Tie-Yan Liu, and Hang Li · 2010
Earlier work this paper cites.
Lower-bounding term frequency normalization
Yuanhua Lv and ChengXiang Zhai · 2011
Earlier work this paper cites.
Relation based term weighting regularization
Hao Wu and Hui Fang · 2012
Earlier work this paper cites.
A theoretical analysis of ndcg ranking measures
Wang Yining, Wang Liwei, Li Yuanzhi, He Di, Chen Wei, and Liu Tie-Yan · 2013
Earlier work this paper cites.
A weighted correlation index for rankings with ties
Sebastiano Vigna · 2015
Earlier work this paper cites.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
Earlier work this paper cites.
“why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
A study on the interpretability of neural retrieval models using DeepSHAP
Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand · 2019
Cited alongside, same era.
Correlation, prediction and ranking of evaluation metrics in information retrieval
Soumyajit Gupta, Mucahid Kutlu, Vivek Khetan, and Matthew Lease · 2019
Cited alongside, same era.
Neuralndcg: Direct optimisation of a ranking metric via differentiable relaxation of sorting
Przemyslaw Pobrotyn and Radoslaw Bialobrzeski · 2021
Later among the works it cites.
Explaining documents’ relevance to search queries
Razieh Rahimi, Youngwoo Kim, Hamed Zamani, and James Allan · 2021
Later among the works it cites.
Towards axiomatic explanations for neural ranking models
Michael Völske, Alexander Bondarenko, Maik Fröbe, Benno Stein, Jaspreet Singh, Matthias Hagen, and Avishek Anand · 2021
Later among the works it cites.
Explain and predict, and then predict again
Zijian Zhang, Koustav Rudra, and Avishek Anand · 2021
Later among the works it cites.
Local explanations of global rankings: insights for competitive rankings
Hadis Anahideh and Nasrin Mohabbati-Kalejahi · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rodrigo Nogueira and Kyunghyun Cho · 2019
Cited alongside, same era.
Exs: Explainable search using local model agnostic interpretability
Jaspreet Singh and Avishek Anand · 2019
Cited alongside, same era.
Lirme: locally interpretable ranking model explanation
Manisha Verma and Debasis Ganguly · 2019
Cited alongside, same era.
Explaining monotonic ranking functions
Abraham Gale and Amélie Marian · 2020
Cited alongside, same era.
Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
One word at a time: adversarial attacks on retrieval models
Nisarg Raval and Manisha Verma · 2020
Cited alongside, same era.
Avishek Anand, Lijun Lyu, Maximilian Idahl, Yumeng Wang, Jonas Wallat, and Zijian Zhang · 2022
Later among the works it cites.
Finding inverse document frequency information in bert
Jaekeol Choi, Euna Jung, Sungjun Lim, and Wonjong Rhee · 2022
Later among the works it cites.
Explaining preferences with shapley values
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, and Dino Sejdinovic · 2022
Later among the works it cites.
https://microsoft.github.io/msmarco/ , 2016
MS MARCO: Microsoft Machine Reading Comprehension · 2023
Later among the works it cites.
Rank-LIME: Local model-agnostic feature attribution for learning to rank
Tanya Chowdhury, Razieh Rahimi, and James Allan · 2023
Later among the works it cites.
Fine-tuning llama for multi-stage text retrieval
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Prada: practical black-box adversarial attacks against neural ranking models
Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten De Rijke, Yixing Fan, and Xueqi Cheng · 2023
Later among the works it cites.
Rankingshap–listwise feature attribution explanations for ranking models
Maria Heuss, Maarten de Rijke, and Avishek Anand · 2024
Closest in time.
Sharp: Explaining rankings with shapley values
Venetia Pliatsika, Joao Fonseca, Tilun Wang, and Julia Stoyanovich · 2024
Closest in time.