Graph-Based Detection Of Shilling Attacks In Recommender Systems
Zhuo Zhang, Sanjeev R. Kulkarni

Collaborative filtering has been widely used in recommender
systems as a method to recommend items to users.
However, by using knowledge of the recommendation
algorithm, shilling attackers can generate fake profiles
to increase or decrease the popularity of a targeted set
of items. In this paper, we present a method to make
recommender systems resistant to these attacks in the case
that the attack profiles are highly correlated with each
other. We formulate the problem as finding a maximum
submatrix in the similarity matrix. We search for the
maximum submatrix by transforming the problem into a
graph and merging nodes by heuristic functions or finding
the largest component. Experimental results show that
the proposed approach can improve detection precision
compared to state of art methods.