This paper reviews the research trends that link the advanced technical aspects of
recommendation systems that are used in various service areas and the business aspects of …

Deep learning models on graphs have achieved remarkable performance in various graph
analysis tasks, eg, node classification, link prediction, and graph clustering. However, they …

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 …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

Inductive representation learning on temporal graphs is an important step toward salable
machine learning on real-world dynamic networks. The evolving nature of temporal dynamic …

Despite the promising representation learning of graph neural networks (GNNs), the
supervised training of GNNs notoriously requires large amounts of labeled data from each …

Knowledge graphs capture structured information and relations between a set of entities or
items. As such knowledge graphs represent an attractive source of information that could …

Network embedding (or graph embedding) has been widely used in many real-world
applications. However, existing methods mainly focus on networks with single-typed …

Graph embedding methods produce unsupervised node features from graphs that can then
be used for a variety of machine learning tasks. However, modern graph datasets contain …