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Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention.
Least squares quantization in PCM
Stuart Lloyd. 1982 · 1982
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Introduction to reinforcement learning . Vol. 135
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web
Shyong K Lam and John Riedl. 2004 · 2004
Earlier work this paper cites.
Reinforcement learning: A tutorial survey and recent advances
Abhijit Gosavi. 2009 · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) . 681–688
Max Welling and Yee W Teh. 2011 · 2011
Earlier work this paper cites.
HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 985–993
Zhiang Wu, Junjie Wu, Jie Cao, and Dacheng Tao. 2012 · 2012
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
Cross-domain recommender systems
Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, and Paolo Cremonesi. 2015 · 2015
Earlier work this paper cites.
Deep reinforcement learning in large discrete action spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, and Ben Coppin. 2015 · 2015
Cited alongside, same era.
Catch the black sheep: unified framework for shilling attack detection based on fraudulent action propagation. In Twenty-Fourth International Joint Conference on Artificial Intelligence
Yongfeng Zhang, Yunzhi Tan, Min Zhang, Yiqun Liu, Tat-Seng Chua, and Shaoping Ma. 2015 · 2015
Cited alongside, same era.
Data poisoning attacks on factorization-based collaborative filtering. In Advances in neural information processing systems . 1885–1893
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik. 2016 · 2016
Cited alongside, same era.
Deep reinforcement learning: A brief survey
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017 · 2017
Cited alongside, same era.
Poisoning attacks to graph-based recommender systems. In Proceedings of the 34th Annual Computer Security Applications Conference . 381–392
Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, and Jia Liu. 2018 · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 1040–1048
Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin. 2018 · 2018
Later among the works it cites.
Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2847–2856
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. 2018 · 2018
Later among the works it cites.
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Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . International World Wide Web Conferences Steering Committee, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Fake Co-visitation Injection Attacks to Recommender Systems.. In NDSS
Guolei Yang, Neil Zhenqiang Gong, and Ying Cai. 2017 · 2017
Cited alongside, same era.
Hybrid attacks on model-based social recommender systems
Junliang Yu, Min Gao, Wenge Rong, Wentao Li, Qingyu Xiong, and Junhao Wen. 2017 · 2017
Cited alongside, same era.
Shilling attack detection using rated item correlation for collaborative filtering. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 3553–3558
Keke Chen, Patrick PK Chan, and Daniel S Yeung. 2018 · 2018
Cited alongside, same era.
Adversarial Attack on Graph Structured Data. In Proceedings of the 35th International Conference on Machine Learning, PMLR , Vol. 80
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018 · 2018
Cited alongside, same era.
Detecting shilling attacks in recommender systems based on analysis of user rating behavior
Hongyun Cai and Fuzhi Zhang. 2019 · 2019
Later among the works it cites.
Large-scale interactive recommendation with tree-structured policy gradient. In AAAI
Haokun Chen, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, and Yong Yu. 2019 · 2019
Later among the works it cites.
Adversarial attacks on an oblivious recommender. In Proceedings of the 13th ACM Conference on Recommender Systems . 322–330
Konstantina Christakopoulou and Arindam Banerjee. 2019 · 2019
Later among the works it cites.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Later among the works it cites.
Deep reinforcement learning for search, recommendation, and online advertising: a survey
Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin. 2019 · 2019
Later among the works it cites.