Shilling Attack Models in Recommender System

Recommender systems which are based on collaborative filtering are vulnerable to "shilling attacks" due to their open nature. Shillers inject a few unscrupulous "shilling profiles" into the database of ratings for altering the system's recommendation, due to which some inappropriate items are recommended by the system. In this paper, we simulated shilling attacks namely random, average, bandwagon and segment on Movie-Lens(1) dataset, which focused on a set of users having similar interests. Biased ratings of the items are also introduced in the system. The results show that although segment attack has impact on item based collaborative filtering, still it has higher robustness than user based collaborative filtering approach.