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.

翻译：对比学习（CL）近期在推荐领域获得了显著关注。其无需严重依赖标注数据的学习能力，天然解决了数据稀疏性问题。已有研究发现，对比学习不仅能提升推荐准确性，还会在无意中展现出对噪声的显著鲁棒性。然而，本文识别出基于对比学习的推荐系统存在一个漏洞：与非对比学习同类系统相比，它们更容易受到旨在推广目标项目的投毒攻击。我们的分析指出，对比学习损失导致的表示均匀分散化正是造成这一漏洞的关键因素。我们进一步在理论和实证层面证明，对比学习损失的优化会导致表示谱值趋于平滑。基于这些见解，我们试图揭示针对基于对比学习的推荐系统的潜在投毒攻击。所提出的攻击包含一个双目标框架：其一是通过诱导更平滑的谱值分布来放大对比学习损失固有的分散效应（称为分散增强），其二是直接提升目标项目的可见性（称为排名提升）。通过在四个数据集上开展大量实验，我们验证了攻击模型的破坏力。通过揭示这些漏洞，我们旨在推动更鲁棒的基于对比学习的推荐系统的发展。