A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system willbe injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

中文翻译：

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使用Beta分发技术从协作过滤中检测受攻击的项目

推荐系统基于用户和项目，向用户提供适当的项目并有效地帮助用户找到可能感兴趣的项目。最常用的推荐方法是协作过滤。但是，在这种情况下，推荐系统将被注入虚假数据以创建虚假评级来推送或核对特定项目。这将影响用户对推荐系统的信任。毕竟，推荐系统提供可信的推荐项目很重要。因此，存在许多用于检测攻击的算法。在本文中，提出了一种基于beta分布的攻击检测方法。过去，不同的研究人员都假定攻击者仅攻击用户数据中的一个目标项目。这项研究模拟了攻击者在实验中攻击多个目标物品的情况。结果显示检出率超过80％，错误率在16％以内。