Attacking Recommender Systems: A Cost-Benefit Analysis

A work highlights the lack of robustness collaborative recommender systems exhibit against attack. This vulnerability can lead to significantly biased recommendations for target items. Here, we examine such attacks from a cost perspective, focusing on how attack size - that is, the number of ratings...

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Bibliographic Details
Published in:IEEE intelligent systems 2007-05, Vol.22 (3), p.64-68
Main Authors: Hurley, N.J., O'Mahony, M.P., Silvestre, G.C.M.
Format: Article
Language:English
Subjects:
Artificial intelligence
Collaborative work
Cost analysis
Cost benefit analysis
Cybersecurity
Educational institutions
Focusing
Gain
Hackers
Insertion
Mounting
Performance analysis
Ratings
Ratings & rankings
Recommender systems
Robustness
Size measurement
Success factors
Throughput
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Summary:A work highlights the lack of robustness collaborative recommender systems exhibit against attack. This vulnerability can lead to significantly biased recommendations for target items. Here, we examine such attacks from a cost perspective, focusing on how attack size - that is, the number of ratings inserted - affects attack success. We introduce a framework for quantifying the gains attackers realize, taking into account the financial cost of mounting the attack. A cost-benefit analysis of third-party attacks on recommender systems shows that attackers realize profits even when incurring costs associated with rating insertions.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2007.44