Recommender systems have become an integral part of online services due to their ability to
help users locate specific information in a sea of data. However, existing studies show that …

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 …

Anomaly detection for time-series data is crucial in the management of systems for
streaming applications, computational services, and cloud platforms. The majority of current …

To explore the robustness of recommender systems, researchers have proposed various
shilling attack models and analyzed their adverse effects. Primitive attacks are highly …

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 …

Online rating system plays a crucial role in collaborative filtering recommender systems
(CFRSs). However, CFRSs are highly vulnerable to “shilling” attacks in reality. How to …

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 …

With the development of personalized recommendations, information overload has been
alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity 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 …

The existing unsupervised methods for detecting shilling attacks are mostly based on the
rating patterns of users, ignoring the rating behavior difference between genuine users and …