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

该项目将评估相对较少研究的基于模型的推荐系统中的脆弱性，在该系统中，推荐是基于一个将一个项目的评级与其他项目的评级相关联的模型。推荐系统是一种通过过滤潜在的压倒性数量的选项(如卖家提供的所有产品)来识别那些被计算为最感兴趣的产品，从而减少“信息过载”的一种手段。这个项目扩展了对协作推荐系统的研究，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配置文件来影响结果，来基于其他人表达的偏好为个人提供推荐。研究表明，基于特定模型的系统比基于记忆的系统表现出更强的抗推荐攻击能力，在记忆系统中，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.