Recommender systems are widely used in electronic commerce, social media and online streaming services to provide personalized recommendations to the users by exploiting past ratings and interactions. This paper considers the security aspect with quick and accurate detection of attacks by observing the newly created profiles sequentially to prevent the damage which may be incurred by the injection of new profiles with dishonest ratings. The proposed framework consists of a latent variable model, which is trained by a variational EM algorithm, followed by a sequential detection algorithm. The latent variable model generates homogeneous representations of the users given their rating history and mixed data-type attributes such as age and gender. The representations are then exploited to generate univariate statistics to be efficiently used in a CUSUM-like sequential detection algorithm that can quickly detect persistent attacks while maintaining low false alarm rates. We apply our proposed framework to three different real-world datasets and exhibit superior performance in comparison to the existing baseline algorithms for both attack profile and sequential detection. Furthermore, we demonstrate robustness to different attack strategies and configurations.

中文翻译：

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推荐系统中的顺序攻击检测


推荐系统广泛应用于电子商务、社交媒体和在线流媒体服务中，通过利用过去的评级和交互向用户提供个性化推荐。本文考虑了安全方面的问题，通过顺序观察新创建的配置文件来快速准确地检测攻击，以防止注入具有不诚实评级的新配置文件可能造成的损害。所提出的框架由一个潜变量模型组成，该模型由变分 EM 算法训练，然后是顺序检测算法。考虑到用户的评级历史和混合数据类型属性（例如年龄和性别），潜变量模型会生成用户的同质表示。然后利用这些表示来生成单变量统计数据，以便在类似 CUSUM 的顺序检测算法中有效使用，该算法可以快速检测持续攻击，同时保持较低的误报率。我们将我们提出的框架应用于三个不同的现实世界数据集，并且与攻击概况和顺序检测的现有基线算法相比，表现出卓越的性能。此外，我们还展示了对不同攻击策略和配置的鲁棒性。