  Collaborative Filtering (CF) models lie at the core of most recommendation
systems due to their state-of-the-art accuracy. They are commonly adopted in
e-commerce and online services for their impact on sales volume and/or
diversity, and their impact on companies' outcome. However, CF models are only
as good as the interaction data they work with. As these models rely on outside
sources of information, counterfeit data such as user ratings or reviews can be
injected by attackers to manipulate the underlying data and alter the impact of
resulting recommendations, thus implementing a so-called shilling attack. While
previous works have focused on evaluating shilling attack strategies from a
global perspective paying particular attention to the effect of the size of
attacks and attacker's knowledge, in this work we explore the effectiveness of
shilling attacks under novel aspects. First, we investigate the effect of
attack strategies crafted on a target user in order to push the recommendation
of a low-ranking item to a higher position, referred to as user-item attack.
Second, we evaluate the effectiveness of attacks in altering the impact of
different CF models by contemplating the class of the target user, from the
perspective of the richness of her profile (i.e., cold v.s. warm user).
Finally, similar to previous work we contemplate the size of attack (i.e., the
amount of fake profiles injected) in examining their success. The results of
experiments on two widely used datasets in business and movie domains, namely
Yelp and MovieLens, suggest that warm and cold users exhibit contrasting
behaviors in datasets with different characteristics.
