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How Fraudster Detection Contributes to Robust Recommendation. (arXiv:2211.11534v2 [cs.IR] UPDATED)
Nov. 23, 2022, 2:20 a.m. | Yuni Lai, Kai Zhou
cs.CR updates on arXiv.org arxiv.org
The adversarial robustness of recommendation systems under node injection
attacks has received considerable research attention. Recently, a robust
recommendation system GraphRfi was proposed, and it was shown that GraphRfi
could successfully mitigate the effects of injected fake users in the system.
Unfortunately, we demonstrate that GraphRfi is still vulnerable to attacks due
to the supervised nature of its fraudster detection component. Specifically, we
propose a new attack metaC against GraphRfi, and further analyze why GraphRfi
fails under such an attack. …
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