Sequential recommendation approaches have demonstrated remarkable proficiency in modeling user preferences. Nevertheless, they are susceptible to profile pollution attacks (PPA), wherein items are introduced into a user's interaction history deliberately to influence the recommendation list. Since retraining the model for each polluted item is time-consuming, recent PPAs estimate item influence based on gradient directions to identify the most effective attack candidates. However, the actual item representations diverge significantly from the gradients, resulting in disparate outcomes.To tackle this challenge, we introduce an INFluence Function-based Attack approach INFAttack that offers a more accurate estimation of the influence of polluting items. Specifically, we calculate the modifications to the original model using the influence function when generating polluted sequences by introducing specific items. Subsequently, we choose the sequence that has been most significantly influenced to substitute the original sequence, thus promoting the target item. Comprehensive experiments conducted on five real-world datasets illustrate that INFAttack surpasses all baseline methods and consistently delivers stable attack performance for both popular and unpopular items.

翻译：序列推荐方法在建模用户偏好方面已展现出卓越能力。然而，这些方法易受画像污染攻击的影响，即通过向用户交互历史中刻意插入项目以操纵推荐列表。由于针对每个污染项目重新训练模型耗时严重，近期研究通过基于梯度方向估计项目影响力来识别最有效的攻击候选。然而，实际项目表示与梯度方向存在显著偏差，导致攻击效果出现差异。为应对这一挑战，我们提出基于影响函数的攻击方法INFAttack，该方法能更精确地评估污染项目的影响力。具体而言，我们通过引入特定项目生成污染序列时，利用影响函数计算原始模型的参数变化量。随后，我们选择受影响最显著的序列替代原始序列，从而有效提升目标项目的推荐概率。在五个真实数据集上的综合实验表明，INFAttack在所有基线方法中表现最优，且对热门与非热门项目均能保持稳定的攻击性能。