Poisoning Attacks Against Contrastive Recommender Systems
Zongwei Wang Junliang Yu Min Gao Hongzhi Yin Bin Cui Shazia Sadiq
Contrastive learning (CL) has recently gained significant popularity in the
field of recommendation. Its ability to learn without heavy reliance on labeled
data is a natural antidote to the data sparsity issue. Previous research has
found that CL can not only enhance recommendation accuracy but also
inadvertently exhibit remarkable robustness against noise. However, this paper
identifies a vulnerability of CL-based recommender systems: Compared with their
non-CL counterparts, they are even more susceptible to poisoning attacks that
aim to promote target items. Our analysis points to the uniform dispersion of
representations led by the CL loss as the very factor that accounts for this
vulnerability. We further theoretically and empirically demonstrate that the
optimization of CL loss can lead to smooth spectral values of representations.
Based on these insights, we attempt to reveal the potential poisoning attacks
against CL-based recommender systems. The proposed attack encompasses a
dual-objective framework: One that induces a smoother spectral value
distribution to amplify the CL loss's inherent dispersion effect, named
dispersion promotion; and the other that directly elevates the visibility of
target items, named rank promotion. We validate the destructiveness of our
attack model through extensive experimentation on four datasets. By shedding
light on these vulnerabilities, we aim to facilitate the development of more
robust CL-based recommender systems.