Big enough to care not enough to scare! crawling to attack recommender systems

Online recommendation services, such as e-commerce sites, rely on a vast amount of knowledge about users/items that represent an invaluable resource. Part of this acquired knowledge is public and can be accessed by anyone through the Internet. Unfortunately, that same knowledge can be used by competitors or malicious users. A large body of research proposes methods to attack recommender systems, but most of these works assume that the attacker knows or can easily access the rating matrix. In practice, this information is not directly accessible, but can only be gathered via crawling. Considering such real-life limitations, in this paper, we assess the impact of different crawling approaches when attacking a recommendation service. From the crawled information, we mount different shilling attacks. We determine the value of the collected knowledge through the reconstruction of the user/item neighborhood. Our results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction), this will not be enough to mount a successful shilling attack in practice.