Adversarial Recommendation Attacks
Adversarial Recommendation Attacks is a systematic method designed to manipulate recommendation systems by crafting malicious inputs or perturbations that exploit model vulnerabilities, thereby degrading recommendation accuracy or promoting undesirable items without explicit access to the model's internal parameters.
86
Publications
5.3K
Citations
264
Authors
117
Institutions
Publications per year
2017–2026
53
Variants
Adversarial Attacks On Recommender Systems
Parents
86
Toward trustworthy recommender systems
Bamshad Mobasher, Robin Burke, Runa Bhaumik et al. · ACM Transactions on Internet Technology · 2007 · 465 citations
Shilling attacks against recommender systems: a comprehensive survey
İhsan Güneş, Cihan Kaleli, Alper Bilge et al. · Artificial Intelligence Review · 2012 · 327 citations
Classification features for attack detection in collaborative recommender systems
Robin Burke, Bamshad Mobasher, Chad Williams et al. · 2006 · 251 citations
Poisoning Attacks to Graph-Based Recommender Systems
Minghong Fang, Guolei Yang, Neil Zhenqiang Gong et al. · 2018 · 215 citations · Full text
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117

DePaul University
Chicago, United States
9 h-index
30 publications
5.2K citations

City University of Hong Kong
Hong Kong, Hong Kong
4 h-index
7 publications
228 citations

Vanderbilt University
Nashville, United States
4 h-index
5 publications
339 citations

Polytechnic University of Bari
Bari, Italy
4 h-index
16 publications
562 citations
University of Technology Sydney
Sydney, Australia
4 h-index
4 publications
161 citations

Alibaba Group (China)
Hangzhou, China
3 h-index
7 publications
298 citations
University College Dublin
Dublin, Ireland
3 h-index
7 publications
372 citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
9 publications
330 citations
1–8 of 117
4 publications · 87 citations
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Repository · 4 publications · 300 citations
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Conference · 3 publications · 129 citations
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2 publications · 380 citations
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TPAMI · 2 publications · 63 citations
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