  Recommender system has attracted much attention during the past decade. Many
attack detection algorithms have been developed for better recommendations,
mostly focusing on shilling attacks, where an attack organizer produces a large
number of user profiles by the same strategy to promote or demote an item. This
work considers a different attack style: unorganized malicious attacks, where
attackers individually utilize a small number of user profiles to attack
different items without any organizer. This attack style occurs in many real
applications, yet relevant study remains open. We first formulate the
unorganized malicious attacks detection as a matrix completion problem, and
propose the Unorganized Malicious Attacks detection (UMA) approach, a proximal
alternating splitting augmented Lagrangian method. We verify, both
theoretically and empirically, the effectiveness of our proposed approach.
