Abstract
In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naïve Bayesian classifier-based collaborative filtering algorithm is examined. Real data-based experiments are conducted and each attack type’s performance is explicated. Since existing measures, which are used to assess the success of shilling attacks, do not work on binary data, a new evaluation metric is proposed. Empirical outcomes show that it is possible to manipulate binary rating-based recommender systems’ predictions by inserting malicious user profiles. Hence, it is shown that naïve Bayesian classifier-based collaborative filtering scheme is not robust against shilling attacks.
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Kaleli, C., Polat, H. (2013). Robustness Analysis of Naïve Bayesian Classifier-Based Collaborative Filtering. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_19
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DOI: https://doi.org/10.1007/978-3-642-39878-0_19
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