Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-
Euclidean structural data, they are difficult to be deployed in real applications due to the …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Machine Unlearning (MU) algorithms have become increasingly critical due to the
imperative adherence to data privacy regulations. The primary objective of MU is to erase …

Graph Neural Networks (GNNs) have demonstrated a great representation learning
capability on graph data and have been utilized in various downstream applications …

Graph Neural Networks (GNNs) conduct message passing which aggregates local
neighbors to update node representations. Such message passing leads to scalability …

Self-supervised learning (SSL) has gained significant interest in recent years as a solution to
address the challenges posed by sparse and noisy data in recommender systems. Despite …