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 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...
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...
Abstract. Online recommender systems are a common target of attack. Existing research has focused on automated manipulation of recommender systems through the creation of shill acc...
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...