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Recently, recommender systems have achieved promising performances and become one of the most widely used web applications.
Explaining Collaborative Filtering Recommendations
Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl · 2000
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Evaluation of Item-Based Top-N Recommendation Algorithms
George Karypis · 2001
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Item-Based Collaborative Filtering Recommendation Algorithms
Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl · 2001
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Item-Based Top-N Recommendation Algorithms
Mukund Deshpande and George Karypis · 2004
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SVD-based Collaborative Filtering with Privacy
Huseyin Polat and Wenliang Du · 2005
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Content-Based Recommendation Systems
Michael J. Pazzani and Daniel Billsus · 2007
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Probabilistic Matrix Factorization
Ruslan Salakhutdinov and Andriy Mnih · 2007
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Collaborative Filtering Recommender Systems
J. Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen · 2007
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Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
Yehuda Koren · 2008
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Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Collaborative Filtering with Temporal Dynamics
Yehuda Koren · 2009
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Second Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec2011)
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik · 2011
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Quantifying Location Privacy
Reza Shokri, Georgios Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux · 2011
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Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A Konstan · 2015
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
Ruining He and Julian McAuley · 2016
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Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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walk2friends: Inferring Social Links from Mobility Profiles
Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang · 2017
Cited alongside, same era.
A Neural Collaborative Filtering Model with Interaction-based Neighborhood
Ting Bai, Ji-Rong Wen, Jun Zhang, and Wayne Xin Zhao · 2017
Cited alongside, same era.
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Grag · 2017
Cited alongside, same era.
Neural Collaborative Filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
Cited alongside, same era.
Membership Inference Attacks Against Machine Learning Models
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN
Zheng Li, Chengyu Hu, Yang Zhang, and Shanqing Guo · 2019
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Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
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White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou · 2019
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
Cited alongside, same era.
Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
SoK: Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2018
Cited alongside, same era.
Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Cited alongside, same era.
Attentive Recurrent Social Recommendation
Peijie Sun, Le Wu, and Meng Wang · 2018
Cited alongside, same era.
Auditing Data Provenance in Text-Generation Models
Congzheng Song and Vitaly Shmatikov · 2019
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Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2020
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GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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Label-Only Membership Inference Attacks
Christopher A. Choquette Choo, Florian Tramèr, Nicholas Carlini, and Nicolas Papernot · 2020
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High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot · 2020
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Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
Klas Leino and Matt Fredrikson · 2020
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Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation
Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, and Meng Wang · 2020
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A Hybrid Recommender System for Recommending Relevant Movies Using An Expert System
Bogdan Walek and Vladimir Fojtik · 2020
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Stealing Links from Graph Neural Networks
Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang · 2021
Closest in time.
Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini · 2021
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Membership Privacy for Machine Learning Models Through Knowledge Transfer
Virat Shejwalkar and Amir Houmansadr · 2021
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