Online recommender systems have been shown to be vulnerable to group shilling attacks in
which attackers of a shilling group collaboratively inject fake profiles with the aim of …

Recommender systems (RS) have become an essential component of web services due to
their excellent performance. Despite their great success, RS have proved to be vulnerable to …

Over the past decade, many approaches have been presented to detect shilling attacks in
collaborative recommender systems. However, these approaches focus mainly on detecting …

The effectiveness of recommendation systems heavily relies on accurately predicting user
ratings for items based on user preferences and item attributes derived from ratings and …

Recommender systems are vulnerable to injective attacks, which inject limited fake users
into the platforms to manipulate the exposure of target items to all users. In this work, we …

In recent years, recommender systems have become a ubiquitous part of our daily lives,
while they suffer from a high risk of being attacked due to the growing commercial and social …

Attackers attempt to bias the outputs of collaborative recommender systems by maliciously
rating goods or services. To detect such attacks, many deep learning-based detection …

Due to the openness of the recommender systems, the attackers are likely to inject a large
number of fake profiles to bias the prediction of such systems. The traditional detection …

In the detection methods for shilling attacks, the supervised methods require labeled
samples to train the classifiers. Due to lack of the labeled sample profiles in real scenarios …

Recommender systems can effectively improve user experience, but they are vulnerable to
shilling attacks due to their open nature. Attackers inject fake user profiles to destroy the …