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Federated recommendation systems can provide good performance without collecting users' private data, making them attractive.
BPR: bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2009
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Factorization machines
Steffen Rendle · 2010
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2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011)
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John C. Duchi, Elad Hazan, and Yoram Singer · 2011
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Collaborative topic modeling for recommending scientific articles
Chong Wang and David M. Blei · 2011
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Collaborative topic regression with social matrix factorization for recommendation systems
Sanjay Purushotham and Yan Liu · 2012
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Social media retrieval, chapter privacy in recommender systems, 2013
M Beye, A Jeckmans, Z Erkin, Q Tang, P Hartel, and I Lagendijk · 2013
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FISM: factored item similarity models for top-n recommender systems
Santosh Kabbur, Xia Ning, and George Karypis · 2013
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Alexandrin Popescul, Lyle H Ungar, David M Pennock, and Steve Lawrence · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton · 2013
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Collaborative topic regression with social regularization for tag recommendation
Hao Wang, Binyi Chen, and Wu-Jun Li · 2013
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Collaborative deep learning for recommender systems
Hao Wang, Naiyan Wang, and Dit-Yan Yeung · 2015
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Recommender systems and their security concerns
Jun Wang and Qiang Tang · 2015
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Optimization in online content recommendation services: Beyond click-through rates
Omar Besbes, Yonatan Gur, and Assaf Zeevi · 2016
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Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik · 2016
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Collaborative denoising auto-encoders for top-n recommender systems
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
Modeling dynamic missingness of implicit feedback for recommendation
Menghan Wang, Mingming Gong, Xiaolin Zheng, and Kun Zhang · 2018
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Phocas: Dimensional byzantine-resilient stochastic gradient descent
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta · 2018
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On the convergence of adaptive gradient methods for nonconvex optimization
Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, and Quanquan Gu · 2018
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Federated collaborative filtering for privacy-preserving personalized recommendation system
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan · 2019
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A little is enough: Circumventing defenses for distributed learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg · 2019
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Dropoutnet: Addressing cold start in recommender systems
Maksims Volkovs, Guang Wei Yu, and Tomi Poutanen · 2017
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
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A game-theoretic approach to recommendation systems with strategic content providers
Omer Ben-Porat and Moshe Tennenholtz · 2018
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Attentive group recommendation
Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong · 2018
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Asynchronous byzantine machine learning (the case of SGD)
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, and Mahsa Taziki · 2018
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Real-time personalization using embeddings for search ranking at airbnb
Mihajlo Grbovic and Haibin Cheng · 2018
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Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang · 2019
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Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2019
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Learning disentangled representations for recommendation
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu · 2019
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Robust aggregation for federated learning
Venkata Krishna Pillutla, Sham M. Kakade, and Zaïd Harchaoui · 2019
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Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta · 2019
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Practical distributed learning: Secure machine learning with communication-efficient local updates
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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A sufficient condition for convergences of adam and rmsprop
Fangyu Zou, Li Shen, Zequn Jie, Weizhong Zhang, and Wei Liu · 2019
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Learning to detect malicious clients for robust federated learning
Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen · 2020
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Federating recommendations using differentially private prototypes
Mónica Ribero, Jette Henderson, Sinead Williamson, and Haris Vikalo · 2020
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