Building a graph neural network (GNN)-based recommender system without violating user
privacy proves challenging. Existing methods can be divided into federated GNNs and …

Cross-domain sequential recommendation is an important development direction of
recommender systems. It combines the characteristics of sequential recommender systems …

A recommendation system is of vital importance in delivering personalization services,
which often brings continuous dual improvement in user experience and organization …

Federated learning (FL) has attracted growing attention since it allows for privacy-preserving
collaborative training on decentralized clients without explicitly uploading sensitive data to …

In recent years, the development of online social network platforms has led to increased
research efforts in social recommendation systems. Unlike traditional recommendation …

The rapid development of device-edge-cloud collaborative computing techniques has
actively contributed to the popularization and application of intelligent service models. The …

Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a
target domain with sparse data by leveraging rich information in a source domain, thereby …

As customer relationship management becomes increasingly data-driven, cross-domain
recommendation (CDR) systems are critical in leveraging insights from different domains to …

Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging
useful information to tackle the data sparsity problem across domains. Most of latest CDR …

With the explosive growth of the number of online services and the items (ie, products) they
provide, it becomes extremely time-consuming for users to explore their interested products …