Nowadays, due to the increasing amount of data, the use of recommender systems has
increased. Therefore, the quality of the recommendations for the users of these systems is …

Personalization collaborative filtering recommender systems (CFRSs) are the crucial
components of popular E-commerce services. In practice, CFRSs are also particularly …

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 is an emerging recommender system technique that aims guiding
users based on other customers preferences with behavioral similarities. Such …

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) is a popular method for personalizing product recommendations
for e-commerce applications. In order to recommend a product to a user and predict that …

Collaborative recommender systems are highly vulnerable to attack. Attackers can use
automated means to inject a large number of biased profiles into such a system, resulting in …

Collaborative filtering recommender systems are essentially information systems which are
capable of combining the judgment of a large group of people to make personalized …

Robustness analysis research has shown that conventional memory-based recommender
systems are very susceptible to malicious profile-injection attacks. A number of attack …