Shilling Attack Detection is a computational method designed to identify malicious user profiles or fabricated interactions in recommendation systems that artificially manipulate item rankings to benefit specific items or undermine system integrity.
99
Publications
5.3K
Citations
214
Authors
99
Institutions
Publications per year
2017–2026
50
Parents
Publications
99
Shilling recommender systems for fun and profit
Shyong K. Lam, John Riedl · 2004 · 643 citations
Shilling attacks against recommender systems: a comprehensive survey
İhsan Güneş, Cihan Kaleli, Alper Bilge et al. · Artificial Intelligence Review · 2012 · 327 citations
Distortion as a validation criterion in the identification of suspicious reviews
Guangyu Wu, Derek Greene, Barry Smyth et al. · 2010 · 159 citations · Full text
1–5 of 99
Authors
214
- MG
13
h-index
- FZ
12
h-index
- JC
7
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- QX
7
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- JW
6
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1–5 of 214
Institutions
99
Chongqing University
Chongqing, China
14 h-index
52 publications
2.1K citations
Yanshan University
Qinhuangdao, China
12 h-index
30 publications
1.1K citations
Nanjing University of Finance and Economics
Nanjing, China
7 h-index
21 publications
1K citations
Nanjing University of Science and Technology
Nanjing, China
5 h-index
9 publications
180 citations
South China University of Technology
Guangzhou, China
4 h-index
6 publications
119 citations
Beijing University of Posts and Telecommunications
Beijing, China
4 h-index
6 publications
166 citations
Anadolu University
Eskişehir, Türkiye
4 h-index
11 publications
1.6K citations
Henan Normal University
Xinxiang, China
4 h-index
5 publications
83 citations
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Venues
- Knowledge-Based Systems
4 publications · 223 citations
5
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- Security and Communication Networks
3 publications · 72 citations
3
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- PLoS ONE
3 publications · 120 citations
3
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- Soft Computing
2 publications · 53 citations
2
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- Journal of Ambient Intelligence and Humanized Computing
2 publications · 71 citations
2
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1–5