Robustness analysis of naïve Bayesian classifier-based collaborative filtering

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Atıf İçin Kopyala

KALELİ C., Polat H.

Lecture Notes in Business Information Processing, cilt.152, ss.202-209, 2013 (Scopus)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 152
• Basım Tarihi: 2013
• Doi Numarası: 10.1007/978-3-642-39878-0_19
• Dergi Adı: Lecture Notes in Business Information Processing
• Derginin Tarandığı İndeksler: Scopus
• Sayfa Sayıları: ss.202-209
• Anahtar Kelimeler: Naidie;ve bayesian classifier, Prediction, Robustness, Shilling
• Anadolu Üniversitesi Adresli: Evet

Özet

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.