News Recommendation System(NRS) has become a fundamental technology to many online news services. Meanwhile, several studies show that recommendation systems(RS) are vulnerable to data poisoning attacks, and the attackers have the ability to mislead the system to perform as their desires. A widely studied attack approach, injecting fake users, can be applied on the NRS when the NRS is treated the same as the other systems whose items are fixed. However, in the NRS, as each item (i.e. news) is more informative, we propose a novel approach to poison the NRS, which is to perturb contents of some browsed news that results in the manipulation of the rank of the target news. Intuitively, an attack is useless if it is highly likely to be caught, i.e., exposed. To address this, we introduce a notion of the exposure risk and propose a novel problem of attacking a history news dataset by means of perturbations where the goal is to maximize the manipulation of the target news rank while keeping the risk of exposure under a given budget. We design a reinforcement learning framework, called TDP-CP, which contains a two-stage hierarchical model to reduce the searching space. Meanwhile, influence estimation is also applied to save the time on retraining the NRS for rewards. We test the performance of TDP-CP under three NRSs and on different target news. Our experiments show that TDP-CP can increase the rank of the target news successfully with a limited exposure budget.

本文介绍了一种新型的针对新闻推荐系统的数据攻击方式，通过扰动已浏览新闻的内容来操纵目标新闻的排名，同时提出了曝光风险的概念，并设计了一套基于强化学习的 TDP-CP 框架来减少搜索空间和评估影响。实验结果表明，TDP-CP 可以在有限的曝光预算内成功地提高目标新闻的排名。

新闻推荐系统受到内容扰动的有针对性数据污染攻击