Abstract
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.
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This work was supported by the European Commission under the Horizon 2020 Programme (H2020), as part of the LOCARD project (Grant Agreement no. 832735).
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Appendices
A Neighborhood Reconstruction: User-Based with Cosine Similarity
In Fig. 7, we depict the results for all four considered datasets for the neighborhood reconstruction when using user-based cosine similarity.
Neighborhood reconstruction using user-based cosine similarity. The results are the average (± standard deviation) over five randomly selected users. k on the x-axis is the dimension of the considered neighborhood.
B Neighborhood Reconstruction: Item-Based with Pearson’s Correlation
Finally, in Fig. 8, we depict the results for the neighborhood reconstruction when using an item-based Pearson correlation.
Neighborhood reconstruction using item-based Pearson’s correlation. The results are the average (± standard deviation) over five randomly selected average popular items. k on the x-axis is the dimension of the considered neighborhood.
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Aiolli, F., Conti, M., Picek, S., Polato, M. (2020). Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. In: Chen, L., Li, N., Liang, K., Schneider, S. (eds) Computer Security – ESORICS 2020. ESORICS 2020. Lecture Notes in Computer Science(), vol 12309. Springer, Cham. https://doi.org/10.1007/978-3-030-59013-0_9
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