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2005
ACM

Preventing shilling attacks in online recommender systems

12 years 1 months ago
Preventing shilling attacks in online recommender systems
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 evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting recommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retr...
Paul-Alexandru Chirita, Wolfgang Nejdl, Cristian Z
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where WIDM
Authors Paul-Alexandru Chirita, Wolfgang Nejdl, Cristian Zamfir
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