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 …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Federated learning (FL) is a feasible technique to learn personalized recommendation
models from decentralized user data. Unfortunately, federated recommender systems are …

Given the availability of abundant data, deep learning models have been advanced and
become ubiquitous in the past decade. In practice, due to many different reasons (eg …

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

Due to the pivotal role of recommender systems (RS) in guiding customers toward the
purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this …

Adversarial machine learning is the research field investigating vulnerabilities inherent to
machine learning systems' design and ways to defend against them. Recently …

Recommendation systems (RS) are crucial for alleviating the information overload problem.
Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to …

Graph Neural Networks (GNNs) provide a class of powerful architectures that are effective
for graph-based collaborative filtering. Nevertheless, GNNs are known to be vulnerable to …