Collaborative recommender systems are vulnerable to attacks that seek to manipulate recommendations made for target items. The authors examine such attacks from a cost perspective, focusing on the effect that attack size—in terms of the number of ratings inserted during an attack—has on attack success. They present a cost-benefit analysis that shows that attackers can realize profits, even when financial costs are imposed on the insertion of ratings.