Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems ...
Bamshad Mobasher, Robin D. Burke, Chad Williams, R...
Robustness analysis research has shown that conventional memory-based recommender systems are very susceptible to malicious profile-injection attacks. A number of attack models h...
Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recom...
Sheng Zhang, Amit Chakrabarti, James Ford, Fillia ...
Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalize...
Paul-Alexandru Chirita, Wolfgang Nejdl, Cristian Z...