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Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings.
Collaborative recommendation: A robustness analysis
Michael O’Mahony, Neil Hurley, Nicholas Kushmerick, and Guénolé Silvestre · 2004
Earlier work this paper cites.
On the stability of recommendation algorithms
Gediminas Adomavicius and Jingjing Zhang · 2010
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Maximizing stability of recommendation algorithms: A collective inference approach
Gediminas Adomavicius and Jingjing Zhang · 2011
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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
Earlier work this paper cites.
A robust collaborative filtering approach based on user relationships for recommendation systems
Min Gao, Bin Ling, Quan Yuan, Qingyu Xiong, and Linda Yang · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2015
Earlier work this paper cites.
Classification, ranking, and top-k stability of recommendation algorithms
Gediminas Adomavicius and Jingjing Zhang · 2016
Earlier work this paper cites.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow · 2016
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
Recurrent recommender networks
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing · 2017
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Adversarial personalized ranking for recommendation
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua · 2018
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Recurrent neural networks with top-k gains for session-based recommendations
Balázs Hidasi and Alexandros Karatzoglou · 2018
Cited alongside, same era.
Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang · 2018
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Adversarial attacks on an oblivious recommender
Konstantina Christakopoulou and Arindam Banerjee · 2019
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Cosrec: 2d convolutional neural networks for sequential recommendation
An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, and Julian McAuley · 2019
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Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method
R Logesh, V Subramaniyaswamy, D Malathi, N Sivaramakrishnan, and Varadarajan Vijayakumar · 2020
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Sequential-based adversarial optimisation for personalised top-n item recommendation
Jarana Manotumruksa and Emine Yilmaz · 2020
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Practical data poisoning attack against next-item recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao · 2020
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A survey on adversarial recommender systems: From attack/defense strategies to generative adversarial networks
Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra · 2021
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A comparative study on transformer vs rnn in speech applications
Shigeki Karita, Nanxin Chen, Tomoki Hayashi, Takaaki Hori, Hirofumi Inaguma, Ziyan Jiang, Masao Someki, Nelson Enrique Yalta Soplin, Ryuichi Yamamoto, Xiaofei Wang, Shinji Watanabe, Takenori Yoshimura, and Wangyou Zhang · 2019
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Adversarial sampling and training for semi-supervised information retrieval
Dae Hoon Park and Yi Chang · 2019
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Evaluating recommender system stability with influence-guided fuzzing
David Shriver, Sebastian Elbaum, Matthew B Dwyer, and David S Rosenblum · 2019
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang · 2019
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Adversarial examples in modern machine learning: A review
Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, and Archy de Berker · 2019
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Continuous-time sequential recommendation with temporal graph collaborative transformer
Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, and Philip S Yu · 2021
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Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer
Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S Yu · 2021
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Black-box attacks on sequential recommenders via data-free model extraction
Zhenrui Yue, Zhankui He, Huimin Zeng, and Julian McAuley · 2021
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Rank list sensitivity of recommender systems to interaction perturbations
Sejoon Oh, Berk Ustun, Julian McAuley, and Srijan Kumar · 2022
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Rgrecsys: A toolkit for robustness evaluation of recommender systems
Zohreh Ovaisi, Shelby Heinecke, Jia Li, Yongfeng Zhang, Elena Zheleva, and Caiming Xiong · 2022
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Contrastive learning for sequential recommendation
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui · 2022
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