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 …

With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

With recent advancements in graph neural networks (GNN), GNN-based recommender
systems (gRS) have achieved remarkable success in the past few years. Despite this …

We investigate whether model extraction can be used to 'steal'the weights of sequential
recommender systems, and the potential threats posed to victims of such attacks. This type …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Recent studies have shown that recommender systems are vulnerable, and it is easy for
attackers to inject well-designed malicious profiles into the system, resulting in biased …

Recent studies have shown that deep neural networks-based recommender systems are
vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user …

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 …

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