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 …

Abstract Machine learning classification algorithms are currently widely used. One of the
main problems faced by classification algorithms is the problem of unbalanced data sets …

Federated recommendation (FedRec) can train personalized recommenders without
collecting user data, but the decentralized nature makes it susceptible to poisoning attacks …

Federated Recommendation (FR) has received con-siderable popularity and attention in the
past few years. In FR, for each user, its feature vector and interaction data are kept locally on …

As an important means to solve information overload, recommender systems have been
widely applied in many fields, such as e-commerce and advertising. However, recent studies …

Classification problems of unbalanced data sets are commonplace in industrial production
and medical research fields. Different approaches have been proposed to handle these …

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 …

Shilling attacks against collaborative filtering (CF) models are characterized by several fake
user profiles mounted on the system by an adversarial party to harvest recommendation …