In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …

Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate
items because of their promising ability to extract features from user–item interactions and …

Attackers attempt to bias the outputs of collaborative recommender systems by maliciously
rating goods or services. To detect such attacks, many deep learning-based detection …

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

Recommendation systems (RS) have become indispensable tools for web services to
address information overload, thus enhancing user experiences and bolstering platforms' …

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 …

The attacker's malicious behavior of injecting well-designed adversarial examples (ie, fake
users) into recommender systems will severely affect the security of systems. It's difficult to …

Over the past two decades, many studies have devoted a good deal of attention to detect
injection attacks in recommender systems. However, most of the studies mainly focus on …

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