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 …

Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

Depression is one of the most common mental illnesses, and the symptoms shown by
patients are different, making it difficult to diagnose in the process of clinical practice and …

Federated recommendation can potentially alleviate the privacy concerns in collecting
sensitive and personal data for training personalized recommendation systems. However, it …

Sequential recommendation techniques are considered to be a promising way of providing
better user experience in mobile commerce by learning sequential interests within user …

Federated learning has recently been applied to recommendation systems to protect user
privacy. In federated learning settings, recommendation systems can train recommendation …

In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …

Recently, federated multi-view clustering (FedMVC) has emerged as a powerful tool to
uncover complementary cluster structures across distributed clients, gaining significant …

Depression is one of the most common mental illness problems, and the symptoms shown
by patients are not consistent, making it difficult to diagnose in the process of clinical practice …

Along with the rapid expansion of information technology and digitalization of health data,
there is an increasing concern on maintaining data privacy while garnering the benefits in …