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Can machine learning models for recommendation be easily fooled? While the question has been answered for hand-engineered fake user profiles, it has not been explored for machine learned adversarial attacks.
Completely derandomized self-adaptation in evolution strategies
Nikolaus Hansen and Andreas Ostermeier · 2001
Earlier work this paper cites.
Promoting recommendations: An attack on collaborative filtering
Michael P O’Mahony, Neil J Hurley, and Guenole CM Silvestre · 2002
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Adversarial classification
Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, Deepak Verma, et al · 2004
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Shilling recommender systems for fun and profit
Shyong K Lam and John Riedl · 2004
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Collaborative recommendation: A robustness analysis
Michael O’Mahony, Neil Hurley, Nicholas Kushmerick, and Guénolé Silvestre · 2004
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Influence in ratings-based recommender systems: An algorithm-independent approach
Al Mamunur Rashid, George Karypis, and John Riedl · 2005
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Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams · 2007
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Probabilistic matrix factorization
Andriy Mnih and Ruslan R Salakhutdinov · 2008
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The information cost of manipulation-resistance in recommender systems
Paul Resnick and Rahul Sami · 2008
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Optimal algorithms for online convex optimization with multi-point bandit feedback
Alekh Agarwal, Ofer Dekel, and Lin Xiao · 2010
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Stability of recommendation algorithms
Gediminas Adomavicius and Jingjing Zhang · 2012
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Stochastic recursive algorithms for optimization: simultaneous perturbation methods
Shalabh Bhatnagar, HL Prasad, and LA Prashanth · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Cited alongside, same era.
Robust collaborative recommendation
Robin Burke, Michael P O�Mahony, and Neil J Hurley · 2015
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Optimal rates for zero-order convex optimization: The power of two function evaluations
John C Duchi, Michael I Jordan, Martin J Wainwright, and Andre Wibisono · 2015
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
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Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Attack-resistant recommender systems
Charu C Aggarwal · 2016
Cited alongside, same era.
Local item-item models for top-n recommendation
Evangelia Christakopoulou and George Karypis · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francois Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
cleverhans v0. 1: an adversarial machine learning library
Ian Goodfellow, Nicolas Papernot, Patrick McDaniel, R Feinman, F Faghri, A Matyasko, K Hambardzumyan, YL Juang, A Kurakin, R Sheatsley, et al · 2016
Cited alongside, same era.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2016
Cited alongside, same era.
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
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Beyond globally optimal: Focused learning for improved recommendations
Alex Beutel, Ed H Chi, Zhiyuan Cheng, Hubert Pham, and John Anderson · 2017
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Visually-aware fashion recommendation and design with generative image models
Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley · 2017
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi · 2017
Later among the works it cites.
Irgan: A minimax game for unifying generative and discriminative information retrieval models
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang · 2017
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Local latent space models for top-n recommendation
Evangelia Christakopoulou and George Karypis · 2018
Closest in time.
Making machine learning robust against adversarial inputs
Ian Goodfellow, Patrick McDaniel, and Nicolas Papernot · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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