Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles. (arXiv:2206.11433v1 [cs.IR])

Web: http://arxiv.org/abs/2206.11433

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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