CT-ISG: Collaborative Research: Detecting and Preventing Attacks in Recommendation Systems

该项目将评估相对未研究的基于模型的推荐系统中的漏洞，其中推荐是基于一个模型，该模型将一个项目的评级与其他项目的评级相关联。 推荐系统是一种减少“信息过载”的手段，它通过过滤可能大量的选项（例如卖家提供的所有产品）来识别那些被计算为最感兴趣的选项。 该项目扩展了对协作推荐系统的研究，该系统基于其他人表达的偏好为个人提供推荐，通过调查这些系统的恶意操纵问题，例如，攻击者试图用有偏见或伪造的评级配置文件来影响结果。 研究表明，一个特定的基于模型的系统比基于记忆的系统表现出更强的抵抗推荐攻击的能力，在基于记忆的系统中，推荐是基于寻找相似用户或相似项目的原则。此外，这项研究将识别任何以前未知的攻击方法，这些方法可能对基于模型的推荐系统特别有效。

This project will evaluate vulnerabilities in relatively unstudied model-based recommendation systems in which recommendations are based on a model that relates ratings on one item to ratings on other items. Recommendation systems are a means of reducing "information overload" by filtering a potentially overwhelming number of options (such as all the products available from a seller) to identify those calculated to be of greatest interest. This project extends research on collaborative recommendation systems, which base recommendations for an individual on the preferences expressed by other people, by investigating the problem of malicious manipulation of these systems, for example, by an attacker attempting to influence the outcome with biased or faked rating profiles. Research suggests that a specific model-based systems exhibit much more resistant to recommendation attacks than memory-based systems in which recommendations are based on the principle of finding similar users or similar items. Moreover, this research will identify any previously unknown attack methods that might be specifically effective against model-based recommendation systems.