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Modern recommender systems face an increasing need to explain their recommendations.
Extracting information from counterfactual clauses
Patricia A Carpenter · 1973
Earlier work this paper cites.
Information amplified: Memory for counterfactual conditionals
Samuel Fillenbaum · 1974
Earlier work this paper cites.
The simulation heuristic
Daniel Kahneman and Amos Tversky · 1981
Earlier work this paper cites.
Grouplens: An open architecture for collaborative filtering of netnews
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl · 1994
Earlier work this paper cites.
The functional basis of counterfactual thinking
Neal J Roese · 1994
Earlier work this paper cites.
A framework for collaborative, content-based and demographic filtering
Michael J Pazzani · 1999
Earlier work this paper cites.
When possibility informs reality: Counterfactual thinking as a cue to causality
Barbara A Spellman and David R Mandel · 1999
Earlier work this paper cites.
Explaining collaborative filtering recommendations
Jonathan L Herlocker, Joseph A Konstan, and John Riedl · 2000
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
A reflection and evaluation model of comparative thinking
Keith D Markman and Matthew N McMullen · 2003
Earlier work this paper cites.
The psychology of counterfactual thinking
David R Mandel, Denis J Hilton, and Patrizia Ed Catellani · 2005
Earlier work this paper cites.
The relationship between ir effectiveness measures and user satisfaction
Azzah Al-Maskari, Mark Sanderson, and Paul Clough · 2007
Earlier work this paper cites.
Content-based recommendation systems
Michael J Pazzani and Daniel Billsus · 2007
Earlier work this paper cites.
A survey of explanations in recommender systems
Nava Tintarev and Judith Masthoff · 2007
Earlier work this paper cites.
The effects of transparency on trust in and acceptance of a content-based art recommender
Henriette Cramer, Vanessa Evers, Satyan Ramlal, Maarten Van Someren, Lloyd Rutledge, Natalia Stash, Lora Aroyo, and Bob Wielinga · 2008
Earlier work this paper cites.
The functional theory of counterfactual thinking
Kai Epstude and Neal J Roese · 2008
Earlier work this paper cites.
Tagsplanations: explaining recommendations using tags
Jesse Vig, Shilad Sen, and John Riedl · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
Earlier work this paper cites.
Do social explanations work? studying and modeling the effects of social explanations in recommender systems
Amit Sharma and Dan Cosley · 2013
Earlier work this paper cites.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma · 2014
Earlier work this paper cites.
Who also likes it? generating the most persuasive social explanations in recommender systems
Beidou Wang, Martin Ester, Jiajun Bu, and Deng Cai · 2014
Earlier work this paper cites.
We know what you want to buy: a demographic-based system for product recommendation on microblogs
Xin Wayne Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, and Xiaoming Li · 2014
Earlier work this paper cites.
Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering
Yao Wu and Martin Ester · 2015
Earlier work this paper cites.
A probabilistic model for using social networks in personalized item recommendation
Allison JB Chaney, David M Blei, and Tina Eliassi-Rad · 2015
Earlier work this paper cites.
Daily-aware personalized recommendation based on feature-level time series analysis
Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, and Shaoping Ma · 2015
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Trirank: Review-aware explainable recommendation by modeling aspects
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen · 2015
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Learning to rank features for recommendation over multiple categories
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu · 2016
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Crowd-based personalized natural language explanations for recommendations
Shuo Chang, F Maxwell Harper, and Loren Gilbert Terveen · 2016
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Exploring demographic information in social media for product recommendation
Explainable recommendation with fusion of aspect information
Yunfeng Hou, Ning Yang, Yi Wu, and S Yu Philip · 2019
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Counterfactual visual explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Diverse generation for multi-agent sports games
Raymond A Yeh, Alexander G Schwing, Jonathan Huang, and Kevin Murphy · 2019
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Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy
Rory Sayres, Ankur Taly, Ehsan Rahimy, Katy Blumer, David Coz, Naama Hammel, Jonathan Krause, Arunachalam Narayanaswamy, Zahra Rastegar, Derek Wu, et al · 2019
Later among the works it cites.
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Wayne Xin Zhao, Sui Li, Yulan He, Liwei Wang, Ji-Rong Wen, and Xiaoming Li · 2016
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https://www.darpa.mil/program/explainable-artificial-intelligence , 2017
Explainable artificial intelligence (xai) · 2017
Cited alongside, same era.
https://www.newamerica.org/cybersecurity-initiative/digichina/blog/full-translation-chinas-new-generation-artificial-intelligence-development-plan-2017 , 2017
Full translation: China’s ’new generation artificial intelligence development plan’ · 2017
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Interpretable convolutional neural networks with dual local and global attention for review rating prediction
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu · 2017
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Scalable and interpretable product recommendations via overlapping co-clustering
Reinhard Heckel, Michail Vlachos, Thomas Parnell, and Celestine Dünner · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Using explainability for constrained matrix factorization
Behnoush Abdollahi and Olfa Nasraoui · 2017
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Black creators sue youtube, alleging racial discrimination · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Cocox: Generating conceptual and counterfactual explanations via fault-lines
Arjun Akula, Shuai Wang, and Song-Chun Zhu · 2020
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Generating plausible counterfactual explanations for deep transformers in financial text classification
Linyi Yang, Eoin M Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, and Ruihai Dong · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
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Prince: Provider-side interpretability with counterfactual explanations in recommender systems
Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, and Gerhard Weikum · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
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https://www.nytimes.com/2021/09/03/technology/facebook-ai-race-primates.html , 2021
Facebook apologizes after a.i. puts ‘primates’ label on video of black men · 2021
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https://www.theverge.com/2021/8/10/22617972/twitter-photo-cropping-algorithm-ai-bias-bug-bounty-results , 2021
Twitter’s photo-cropping algorithm prefers young, beautiful, and light-skinned faces · 2021
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Do users appreciate explanations of recommendations? an analysis in the movie domain
Thi Ngoc Trang Tran, Viet Man Le, Müslüm Atas, Alexander Felfernig, Martin Stettinger, and Andrei Popescu · 2021
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Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S Weld · 2021
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Counterfactual explanations for neural recommenders
Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy · 2021
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Model-agnostic counterfactual explanations of recommendations
Vassilis Kaffes, Dimitris Sacharidis, and Giorgos Giannopoulos · 2021
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Mixed-privacy forgetting in deep networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
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Machine unlearning for random forests
Jonathan Brophy and Daniel Lowd · 2021
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