Temporal Defenses for Robust Recommendations

Abstract

Recommender systems are vulnerable to attack: malicious users may deploy a set of sybils (pseudonymous, automated entities) to inject ratings in order to damage or modify the output of Collaborative Filtering (CF) algorithms. To protect against these attacks, previous work focuses on designing sybil profile classification algorithms, whose aim is to find and isolate sybils. These methods, however, assume that the full sybil profiles have already been input to the system. Deployed recommender systems, on the other hand, operate over time, and recommendations may be damaged while sybils are still injecting their profiles, rather than only after all malicious ratings have been input. Furthermore, system administrators do not know when their system is under attack, and thus when to run these classification techniques, thus risking to leave their recommender system vulnerable to attacks. In this work, we address the problem of temporal sybil attacks, and propose and evaluate methods for monitoring global, user and item behaviour over time, in order to detect rating anomalies that reflect an ongoing attack.