Fetching the paper…
Reading the bibliography…
Recommender systems play a crucial role in helping users to find their interested information in various web services such as Amazon, YouTube, and Google News.
S. Okura, Y. Tagami, S. Ono, and A. Tajima, “Embedding-based news recommendation for millions of users,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2017, pp. 1933–1942
1942
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks , vol. 2, no. 5, pp. 359–366, 1989
1989
Earlier work this paper cites.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web , 2001, pp. 285–295
2001
Earlier work this paper cites.
S. K. Lam and J. Riedl, “Shilling recommender systems for fun and profit,” in Proceedings of the 13th international conference on World Wide Web . ACM, 2004, pp. 393–402
2004
Earlier work this paper cites.
P.-A. Chirita, W. Nejdl, and C. Zamfir, “Preventing shilling attacks in online recommender systems,” in Proceedings of the 7th annual ACM international workshop on Web information and data management . ACM, 2005, pp. 67–74
2005
Earlier work this paper cites.
M. P. O’Mahony, N. J. Hurley, and G. C. Silvestre, “Recommender systems: Attack types and strategies,” in AAAI , 2005, pp. 334–339
2005
Earlier work this paper cites.
H. Yu, H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman, “Sybilguard: defending against sybil attacks via social networks,” in SIGCOMM , 2006
2006
Earlier work this paper cites.
F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens, “Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation,” IEEE Transactions on knowledge and data engineering , vol. 19, no. 3, pp. 355–369, 2007
2007
Earlier work this paper cites.
B. Mobasher, R. Burke, R. Bhaumik, and J. J. Sandvig, “Attacks and remedies in collaborative recommendation,” IEEE Intelligent Systems , vol. 22, no. 3, pp. 56–63, 2007
2007
Earlier work this paper cites.
B. Mobasher, R. Burke, R. Bhaumik, and C. Williams, “Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness,” ACM Transactions on Internet Technology (TOIT) , vol. 7, no. 4, p. 23, 2007
2007
Earlier work this paper cites.
W. Zeller and E. W. Felten, “Cross-site request forgeries: Exploitation and prevention,” The New York Times , pp. 1–13, 2008
2008
Earlier work this paper cites.
G. Danezis and P. Mittal, “Sybilinfer: Detecting sybil nodes using social networks.” in NDSS , 2009
2009
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston et al. , “The youtube video recommendation system,” in Proceedings of the fourth ACM conference on Recommender systems . ACM, 2010, pp. 293–296
2010
Earlier work this paper cites.
J. A. Calandrino, A. Kilzer, A. Narayanan, E. W. Felten, and V. Shmatikov, “” you might also like:” privacy risks of collaborative filtering,” in 2011 IEEE Symposium on Security and Privacy . IEEE, 2011, pp. 231–246
2011
Earlier work this paper cites.
I. Cantador, P. Brusilovsky, and T. Kuflik, “2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011),” in Proceedings of the 5th ACM conference on Recommender systems , ser. RecSys 2011. New York, NY, USA: ACM, 2011
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 315–323
2011
Earlier work this paper cites.
2012
Cited alongside, same era.
G. Wang, T. Konolige, C. Wilson, X. Wang, H. Zheng, and B. Y. Zhao, “You are how you click: Clickstream analysis for sybil detection,” in USENIX Security , 2013
2013
Cited alongside, same era.
X. Xing, W. Meng, D. Doozan, A. C. Snoeren, N. Feamster, and W. Lee, “Take this personally: Pollution attacks on personalized services,” in Presented as part of the 22nd { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 13) , 2013, pp. 671–686
2013
Cited alongside, same era.
Q. Cao, X. Yang, J. Yu, and C. Palow, “Uncovering large groups of active malicious accounts in online social networks,” in CCS , 2014
2014
Cited alongside, same era.
J. Steinhardt, P. W. W. Koh, and P. S. Liang, “Certified defenses for data poisoning attacks,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 3517–3529
2017
Later among the works it cites.
B. Wang, N. Z. Gong, and H. Fu, “Gang: Detecting fraudulent users in online social networks via guilt-by-association on directed graphs,” in ICDM , 2017
2017
Later among the works it cites.
B. Wang, L. Zhang, and N. Z. Gong, “Sybilscar: Sybil detection in online social networks via local rule based propagation,” in INFOCOM , 2017
2017
Later among the works it cites.
G. Yang, N. Z. Gong, and Y. Cai, “Fake co-visitation injection attacks to recommender systems.” in NDSS , 2017
2017
Later among the works it cites.
M. Fang, G. Yang, N. Z. Gong, and J. Liu, “Poisoning attacks to graph-based recommender systems,” in Proceedings of the 34th Annual Computer Security Applications Conference . ACM, 2018, pp. 381–392
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Z. Gong, M. Frank, and P. Mittal, “Sybilbelief: A semi-supervised learning approach for structure-based sybil detection,” TIFS , 2014
2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Cited alongside, same era.
I. Gunes, C. Kaleli, A. Bilge, and H. Polat, “Shilling attacks against recommender systems: a comprehensive survey,” Artificial Intelligence Review , vol. 42, no. 4, pp. 767–799, 2014
2014
Cited alongside, same era.
F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” Acm transactions on interactive intelligent systems (tiis) , vol. 5, no. 4, pp. 1–19, 2015
2015
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, p. 529, 2015
2015
Cited alongside, same era.
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir et al. , “Wide & deep learning for recommender systems,” in Proceedings of the 1st workshop on deep learning for recommender systems . ACM, 2016, pp. 7–10
2016
Cited alongside, same era.
P. Covington, J. Adams, and E. Sargin, “Deep neural networks for youtube recommendations,” in Proceedings of the 10th ACM conference on recommender systems . ACM, 2016, pp. 191–198
2016
Cited alongside, same era.
B. Li, Y. Wang, A. Singh, and Y. Vorobeychik, “Data poisoning attacks on factorization-based collaborative filtering,” in Advances in neural information processing systems , 2016, pp. 1885–1893
2016
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Pi, Z. Ji, and C. Yang, “A survey of recommender system from data sources perspective,” in 2018 8th International Conference on Management, Education and Information (MEICI 2018) . Atlantis Press, 2018
2018
Later among the works it cites.
Y. Ma, X. Zhu, and J. Hsu, “Data poisoning against differentially-private learners: Attacks and defenses,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence , 2019, pp. 4732–4738
2019
Later among the works it cites.
B. Wang, J. Jia, and N. Z. Gong, “Graph-based security and privacy analytics via collective classification with joint weight learning and propagation,” in NDSS , 2019
2019
Later among the works it cites.
D. Yuan, Y. Miao, N. Z. Gong, Z. Yang, Q. Li, D. Song, Q. Wang, and X. Liang, “Detecting fake accounts in online social networks at the time of registrations,” in CCS , 2019
2019
Later among the works it cites.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Computing Surveys (CSUR) , vol. 52, no. 1, p. 5, 2019
2019
Later among the works it cites.
L. Chen, Y. Xu, F. Xie, M. Huang, and Z. Zheng, “Data poisoning attacks on neighborhood‐based recommender systems,” Transactions on Emerging Telecommunications Technologies , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Fang, N. Z. Gong, and J. Liu, “Influence function based data poisoning attacks to top-n recommender systems,” in Proceedings of The Web Conference 2020 , 2020, pp. 3019–3025
2020
Later among the works it cites.
J. Jia, X. Cao, and N. Z. Gong, “Certified robustness of nearest neighbors against data poisoning attacks,” Arxiv , 2020
2020
Later among the works it cites.
B. Wang, X. Cao, J. Jia, and N. Z. Gong, “On certifying robustness against backdoor attacks via randomized smoothing,” in CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision , 2020
2020
Later among the works it cites.
——, “Intrinsic certified robustness of bagging against data poisoning attacks,” in AAAI , 2021
2021
Closest in time.