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 …

The increasing data privacy concerns in recommendation systems have made federated
recommendations attract more and more attention. Existing federated recommendation …

Modern recommender systems have seen substantial success, yet they remain vulnerable to
malicious activities, notably poisoning attacks. These attacks involve injecting malicious data …

Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients
manipulate the global model by modifying local datasets or sending manipulated model …

Privacy and trust are highly demanding in practical recommendation engines. Although
Federated Learning (FL) has significantly addressed privacy concerns, commercial …

In recent years, recommender systems have become a ubiquitous part of our daily lives,
while they suffer from a high risk of being attacked due to the growing commercial and social …

Modern recommender systems (RS) have profoundly enhanced user experience across
digital platforms, yet they face significant threats from poisoning attacks. These attacks …

Contrastive learning (CL) has recently gained prominence in the domain of recommender
systems due to its great ability to enhance recommendation accuracy and improve model …

Recommender systems are an important tool for information retrieval, which can aid in the
solution of the issue of information overload. Recently, contrastive learning has shown …

Contrastive learning (CL) has recently gained significant popularity in the field of
recommendation. Its ability to learn without heavy reliance on labeled data is a natural …