Robustness analysis of naïve Bayesian classifier-based collaborative filtering
Lecture Notes in Business Information Processing, vol.152, pp.202-209, 2013 (Scopus)
- Publication Type: Article / Article
- Volume: 152
- Publication Date: 2013
- Doi Number: 10.1007/978-3-642-39878-0_19
- Journal Name: Lecture Notes in Business Information Processing
- Journal Indexes: Scopus
- Page Numbers: pp.202-209
- Keywords: Naidie;ve bayesian classifier, Prediction, Robustness, Shilling
- Anadolu University Affiliated: Yes
Abstract
In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naïve Bayesian classifierbased 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. © Springer-Verlag Berlin Heidelberg 2013.