Shilling attacks have been a significant vulnerability to collaborative filtering based
recommender systems recently. There are various studies focusing on detecting shilling …

Although collaborative filtering achieves satisfying performance in recommender systems,
many studies suggest that it is vulnerable by shilling attack aimed to manipulate the …

Collaborative filtering (CF) recommender systems have been shown to be vulnerable to
shilling attacks. How to quickly and effectively detect shilling attacks is a key challenge for …

Collaborative recommender systems have been known to be extremely vulnerable to
shilling attacks. To prevent such attacks, many detection approaches including supervised …

The reliability of the recommender system is highly essential for the continuity of any system.
Fake and malicious users may be spoiling system predictions reliability by inserting and …

Collaborative filtering recommender systems (CFRSs) have already been proved effective to
cope with the information overload problem since they merged in the past two decades …

As the use of recommender systems becomes generalized in society, the interest in varying
the orientation of their recommendations is increasing. There are shilling attacks' strategies …

Collaborative filtering, though a success in the field of recommender systems, is vulnerable
to shilling attacks. These attacks add shilling profiles in the dataset and thus manipulate the …

Recommender systems are highly vulnerable to shilling attacks, both by individuals and
groups. Attackers who introduce biased ratings in order to affect recommendations, have …

The open nature of recommender systems makes them vulnerable to shilling attacks. Biased
ratings are introduced in order to affect recommendations, have been shown to cause great …