In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

The increasing data privacy concerns in recommendation systems have made federated
recommendations attract more and more attention. Existing federated recommendation …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …

The marriage of federated learning and recommender system (FedRec) has been widely
used to address the growing data privacy concerns in personalized recommendation …

Collaborative filtering (CF) based recommender systems are typically trained based on
personal interaction data (eg, clicks and purchases) that could be naturally represented as …

Due to the growing privacy concerns, decentralization emerges rapidly in personalized
services, especially recommendation. Also, recent studies have shown that centralized …

Owing to its nature of scalability and privacy by design, federated learning (FL) has received
increasing interest in decentralized deep learning. FL has also facilitated recent research on …

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …