Fetching the paper…
Reading the bibliography…
In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences.
Collaborative filtering with privacy. In IEEE Symposium on Security and Privacy . 45–57
John Canny. 2002 · 2002
Earlier work this paper cites.
Getting to know you: learning new user preferences in recommender systems. In IUI . 127–134
Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K Lam, Sean M McNee, Joseph A Konstan, and John Riedl. 2002 · 2002
Earlier work this paper cites.
Privacy-preserving collaborative filtering
Huseyin Polat and Wenliang Du. 2005 · 2005
Earlier work this paper cites.
Improving recommendation lists through topic diversification. In WWW . 22–32
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005 · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference . 265–284
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
Inferring privacy information from social networks. In ISI . 154–165
Jianming He, Wesley W Chu, and Zhenyu Victor Liu. 2006 · 2006
Earlier work this paper cites.
Probabilistic matrix factorization. In NeurIPS . 1257–1264
Andriy Mnih and Ruslan R Salakhutdinov. 2008 · 2008
Earlier work this paper cites.
Robust de-anonymization of large datasets. In IEEE Symposium on Security and Privacy . 111–125
A Naranyanan and V Shmatikov. 2008 · 2008
Earlier work this paper cites.
Inferring private information using social network data. In WWW . 1145–1146
Jack Lindamood, Raymond Heatherly, Murat Kantarcioglu, and Bhavani Thuraisingham. 2009 · 2009
Earlier work this paper cites.
Differentially private recommender systems: Building privacy into the netflix prize contenders. In SIGKDD . 627–636
Frank McSherry and Ilya Mironov. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Privacy enhanced recommender system. In SITB . 35–42
Zekeriya Erkin, Michael Beye, Thijs Veugen, and Reginald L Lagendijk. 2010 · 2010
Earlier work this paper cites.
Context-aware recommender systems
Gediminas Adomavicius and Alexander Tuzhilin. 2011 · 2011
Earlier work this paper cites.
" You might also like:" Privacy risks of collaborative filtering. In IEEE symposium on security and privacy . 231–246
Joseph A Calandrino, Ann Kilzer, Arvind Narayanan, Edward W Felten, and Vitaly Shmatikov. 2011 · 2011
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
Earlier work this paper cites.
Differentially private m-estimators. In NeurIPS . 361–369
Jing Lei. 2011 · 2011
Earlier work this paper cites.
BlurMe: Inferring and obfuscating user gender based on ratings. In RECSYS . 195–202
Udi Weinsberg, Smriti Bhagat, Stratis Ioannidis, and Nina Taft. 2012 · 2012
Earlier work this paper cites.
Functional mechanism: regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett. 2012 · 2012
Earlier work this paper cites.
Exploiting innocuous activity for correlating users across sites. In WWW . 447–458
Oana Goga, Howard Lei, Sree Hari Krishnan Parthasarathi, Gerald Friedland, Robin Sommer, and Renata Teixeira. 2013 · 2013
Earlier work this paper cites.
Private traits and attributes are predictable from digital records of human behavior
Michal Kosinski, David Stillwell, and Thore Graepel. 2013 · 2013
Earlier work this paper cites.
Privacy-preserving matrix factorization. In SIGSAC . 801–812
Valeria Nikolaenko, Stratis Ioannidis, Udi Weinsberg, Marc Joye, Nina Taft, and Dan Boneh. 2013 · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
Rappor: Randomized aggregatable privacy-preserving ordinal response. In SIGSAC . 1054–1067
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014 · 2014
Cited alongside, same era.
Joint link prediction and attribute inference using a social-attribute network
Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, and Dawn Song. 2014 · 2014
Cited alongside, same era.
PrivacyCanary: Privacy-aware recommenders with adaptive input obfuscation. In MASCOTS . 453–462
Thivya Kandappu, Arik Friedman, Roksana Boreli, and Vijay Sivaraman. 2014 · 2014
Cited alongside, same era.
Many Facebook Users are Sharing Less Content
Rimma Kats. 2018 · 2018
Later among the works it cites.
Efficient privacy-preserving matrix factorization for recommendation via fully homomorphic encryption
Jinsu Kim, Dongyoung Koo, Yuna Kim, Hyunsoo Yoon, Junbum Shin, and Sungwook Kim. 2018 · 2018
Later among the works it cites.
Heterogeneous information network embedding for recommendation
Chuan Shi, Binbin Hu, Wayne Xin Zhao, and S Yu Philip. 2018 · 2018
Later among the works it cites.
Privacy enhanced matrix factorization for recommendation with local differential privacy
Hyejin Shin, Sungwook Kim, Junbum Shin, and Xiaokui Xiao. 2018 · 2018
Later among the works it cites.
DPNE: Differentially private network embedding. In PAKDD . 235–246
Depeng Xu, Shuhan Yuan, Xintao Wu, and HaiNhat Phan. 2018 · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems. In SIGKDD . 974–983
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Optimal forgery and suppression of ratings for privacy enhancement in recommendation systems
Javier Parra-Arnau, David Rebollo-Monedero, and Jordi Forné. 2014 · 2014
Cited alongside, same era.
Applying differential privacy to matrix factorization. In RECSYS . 107–114
Arnaud Berlioz, Arik Friedman, Mohamed Ali Kaafar, Roksana Boreli, and Shlomo Berkovsky. 2015 · 2015
Cited alongside, same era.
Differentially Private Matrix Factorization. In IJCAI . 57–62
Hua Jingyu, Xia Chang, and Zhong Sheng. 2015 · 2015
Cited alongside, same era.
Fast differentially private matrix factorization. In RECSYS . 171–178
Ziqi Liu, Yu-Xiang Wang, and Alexander Smola. 2015 · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS . 3844–3852
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Cited alongside, same era.
You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors. In USENIX . 979–995
Neil Zhenqiang Gong and Bin Liu. 2016 · 2016
Cited alongside, same era.
Learning graph-based poi embedding for location-based recommendation. In CIKM . 15–24
Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016 · 2016
Cited alongside, same era.
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
MovieLens
2019 · 2019
Later among the works it cites.
Air: Attentional intention-aware recommender systems. In ICDE . 304–315
Tong Chen, Hongzhi Yin, Hongxu Chen, Rui Yan, Quoc Viet Hung Nguyen, and Xue Li. 2019 · 2019
Later among the works it cites.
Streaming session-based recommendation. In SIGKDD . 1569–1577
Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, and Nguyen Quoc Viet Hung. 2019 · 2019
Later among the works it cites.
Differentially private recommender system with autoencoders. In iThings . 450–457
Xiaoqian Liu, Qianmu Li, Zhen Ni, and Jun Hou. 2019 · 2019
Later among the works it cites.
Social influence-based group representation learning for group recommendation. In ICDE . 566–577
Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou. 2019 · 2019
Later among the works it cites.
Inferring Substitutable Products with Deep Network Embedding.. In IJCAI . 4306–4312
Shijie Zhang, Hongzhi Yin, Qinyong Wang, Tong Chen, Hongxu Chen, and Quoc Viet Hung Nguyen. 2019 · 2019
Later among the works it cites.
Privacy-aware recommendation with private-attribute protection using adversarial learning. In WSDM . 34–42
Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, and Huan Liu. 2020 · 2020
Later among the works it cites.
Boosting Recommender Systems with Advanced Embedding Models. In WWW Companion . 385–389
Gjorgjina Cenikj and Sonja Gievska. 2020 · 2020
Later among the works it cites.
Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2020 · 2020
Later among the works it cites.
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. In WWW . 906–916
Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, and Nguyen Quoc Viet Hung. 2020 · 2020
Later among the works it cites.
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. In AAAI
Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. 2020 · 2020
Later among the works it cites.
Enhance Social Recommendation with Adversarial Graph Convolutional Networks
Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, and Lizhen Cui. 2020 · 2020
Later among the works it cites.
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. In SIGIR . 689–698
Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, and Lizhen Cui. 2020 · 2020
Later among the works it cites.