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 …

Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

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

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 …

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

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

Recommendation unlearning is an emerging task to serve users for erasing unusable data
(eg, some historical behaviors) from a well-trained recommender model. Existing methods …

Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired
recommendations via injecting fake users with carefully crafted rating scores into the …

Prediction models can exhibit sensitivity with respect to training data: small changes in the
training data can produce models that assign conflicting predictions to individual data points …