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

该项目将评估相对未经研究的基于模型的推荐系统中的漏洞，其中推荐基于将一个项目的评级与其他项目的评级联系起来的模型。推荐系统是一种减少“信息过载”的方法，它通过过滤潜在的大量选项（比如一个卖家提供的所有产品）来识别那些计算出的最感兴趣的产品。该项目扩展了协作推荐系统的研究，协作推荐系统根据其他人表达的偏好为个人提供推荐，通过调查恶意操纵这些系统的问题，例如，攻击者试图通过有偏见或伪造的评级档案来影响结果。研究表明，特定的基于模型的系统比基于内存的系统对推荐攻击表现出更强的抵抗力，在基于内存的系统中，推荐是基于查找相似用户或相似项目的原则。此外，这项研究将确定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.