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 …

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 …

Today, there is lots of information available over the Internet but it's very difficult to filter out
the required information from this overload of information. Thus a solution to this problem …

In social aware network (SAN) paradigm, the fundamental activities concentrate on
exploring the behavior and attributes of the users. This investigation of user characteristic …

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 …

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

Reliability of a recommender system is extremely substantial for the continuity of the system.
Malicious users may harm the reliability of predictions by injecting fake profiles called …

Collaborative Filtering (CF) models lie at the core of most recommendation systems due to
their state-of-the-art accuracy. They are commonly adopted in e-commerce and online …

Recommender systems based on collaborative filtering are highly vulnerable to data
poisoning attacks, where a determined attacker injects fake users with false user-item …

A recommender system is susceptible to manipulation through the injection of carefully
crafted profiles. Some recent profile identification methods only perform well in specific …