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, Czech Republic, 27 - 28 August 2013, vol.152, pp.202-209 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 152
  • City: Prague
  • Country: Czech Republic
  • Page Numbers: pp.202-209
  • Keywords: Shilling, Naive Bayesian classifier, Robustness, Prediction
  • Anadolu University Affiliated: Yes

Abstract

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.