Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Graph Convolutional Networks (GCNs) have emerged as a hot topic of interest for
collaborative filtering among researchers in the recent past. The research which exists in …

In the realm of Graph Neural Networks (GNNs), which excel at capturing intricate
dependencies in graph-structured data, we address a significant limitation. Most state-of-the …

In recent years, we have witnessed an increase in the amount of published research in the
field of Explainable Recommender Systems. These systems are designed to help users find …

In recent years, the powerful modeling ability of Graph Neural Networks (GNNs) has led to
their widespread use in knowledge-aware recommender systems. However, existing GNN …

Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in
graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their …

Graph Neural Networks (GNNs) have increasingly become an indispensable tool in learning
from graph-structured data, catering to various applications including social network …

With the rapid growth of big data and information all-round the internet, deep learning has
become a solution to improve the quality of Recommender Systems (RS). Adapting to …

The increasing need for data trading across businesses nowadays has created a demand
for data marketplaces. However, despite the intentions of both data providers and …