Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

Graph neural networks (GNNs) encounter significant computational challenges when
handling large-scale graphs, which severely restricts their efficacy across diverse …

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

Large Language Models (LLMs), with their advanced contextual understanding abilities,
have demonstrated considerable potential in enhancing recommendation systems via fine …

Given the sheer volume of contemporary e-commerce applications, recommender systems
(RSs) have gained significant attention in both academia and industry. However, traditional …

As some recent information security legislation endowed users with unconditional rights to
be forgotten by any trained machine learning model, personalised IoT service providers …

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 …

The Point-of-Interest (POI) recommendation system, designed to recommend potential future
visits of users based on their check-in sequences, faces the challenge of data scarcity. This …

The burgeoning volume of graph data presents significant computational challenges in
training graph neural networks (GNNs), critically impeding their efficiency in various …

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