Robustness Analysis of Naive Bayesian Classifier-Based Collaborative Filtering

KALELİ C., Polat H.

14th International Conference on Electronic Commerce and Web Technologies (EC-Web), Prague, Çek Cumhuriyeti, 27 - 28 Ağustos 2013, cilt.152, ss.202-209 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 152
  • Basıldığı Şehir: Prague
  • Basıldığı Ülke: Çek Cumhuriyeti
  • Sayfa Sayıları: ss.202-209
  • Anahtar Kelimeler: Shilling, Naive Bayesian classifier, Robustness, Prediction
  • Anadolu Üniversitesi Adresli: Evet

Özet

In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naive 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 naive Bayesian classifier-based collaborative filtering scheme is not robust against shilling attacks.