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

Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

Large Language Models (LLMs), with their advanced contextual understanding abilities,
have demonstrated considerable potential in enhancing recommendation systems via fine …

Previous federated recommender systems are based on traditional matrix factorization,
which can improve personalized service but are vulnerable to gradient inference attacks …

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

Researchers have successfully adapted the privacy-preserving Federated Learning (FL) to
news recommendation tasks to better protect users' privacy, although typically at the cost of …

Group recommendation systems are widely applied in social media, e-commerce, and
diverse platforms. These systems face challenges associated with data privacy constraints …

With the recent success of large language models, particularly foundation models with
generalization abilities, applying foundation models for recommendations becomes a new …

To provide much better recommendation service, traditional recommender systems collect a
large amount of user information, which, if obtained and analyzed maliciously, can cause …

Traditional POI recommendation systems use a centralized data storage approach to train
models, posing significant risks of privacy breaches. Federated learning offers an effective …