Recommendation systems have required centralized storage of user data, but due to privacy
concerns, recent studies adopted federated learning (FL) that discloses intermediate …

Recommender systems are ubiquitous yet often difficult for users to control, and adjust if
recommendation quality is poor. This has motivated conversational recommender systems …

Cyber–physical systems (CPS), as the cornerstone of smart city, has been attracting great
interest from academia and industry. It aims to monitor/control physical components via …

Synthetic data are computer-generated data that mimic and substitute empirical
observations without directly corresponding to real-world phenomena. Widely used in …

Recent years have witnessed the great success of self-supervised learning (SSL) in
recommendation systems. However, SSL recommender models are likely to suffer from …

The actual collection of tabular data for sharing involves confidentiality and privacy
constraints, leaving the potential risks of machine learning for interventional data analysis …

The growth of data publishing, sharing, and mining mechanisms in various fields of industry
and science has led to an increase in the flow of data, making it an important asset that …

Cross-domain recommendation (CDR) is a widely used methodology in recommender
systems to combat data sparsity. It leverages user data across different domains or platforms …

Synthetic data has been widely applied in the real world recently. One typical example is the
creation of synthetic data for privacy concerned datasets. In this scenario, synthetic data …

While recommender systems have become an integral component of the Web experience,
their heavy reliance on user data raises privacy and security concerns. Substituting user …