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 …

The increasing concern for privacy and the use of machine learning on personal data has
led researchers to introduce new approaches to machine learning. Federated learning is …

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in
Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide …

With the growing number of Location-Based Social Networks, privacy-preserving point-of-
interest (POI) recommendation has become a critical challenge when helping users discover …

Owing to the nature of privacy protection, feder-ated recommender systems (FedRecs) have
garnered increasing interest in the realm of on-device recommender systems. However …

As an indispensable personalized service in Location-based Social Networks (LBSNs), the
next Point-of-Interest (POI) recommendation aims to help people discover attractive and …

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

The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted the
importance of effective next Point-of-Interest (POI) recommendations, which leverage …

With the raised privacy concerns and rigorous data regulations, federated learning has
become a hot collaborative learning paradigm for the recommendation model without …

To make room for privacy and efficiency, the deployment of many recommender systems is
experiencing a shift from central servers to personal devices, where the federated …