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Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles. (arXiv:2206.11433v1 [cs.IR])
June 24, 2022, 1:20 a.m. | Chen Lin, Si Chen, Meifang Zeng, Sheng Zhang, Min Gao, Hui Li
cs.CR updates on arXiv.org arxiv.org
Due to the pivotal role of Recommender Systems (RS) in guiding customers
towards the purchase, there is a natural motivation for unscrupulous parties to
spoof RS for profits. In this paper, we study Shilling Attack where an
adversarial party injects a number of fake user profiles for improper purposes.
Conventional Shilling Attack approaches lack attack transferability (i.e.,
attacks are not effective on some victim RS models) and/or attack invisibility
(i.e., injected profiles can be easily detected). To overcome these issues, …
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