Preventing shilling attacks in online recommender systems

  title={Preventing shilling attacks in online recommender systems},
  author={Paul-Alexandru Chirita and Wolfgang Nejdl and Cristian Zamfir},
Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and… CONTINUE READING
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