With the recent success of graph convolutional networks (GCNs), they have been widely
applied for recommendation, and achieved impressive performance gains. The core of …

With the tremendous success of Graph Convolutional Networks (GCNs), they have been
widely applied to recommender systems and have shown promising performance. However …

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 …

Abstract\textbf {G} raph\textbf {C} onvolutional\textbf {N} etwork (\textbf {GCN}) is widely used
in graph data learning tasks such as recommendation. However, when facing a large graph …

As much as Graph Convolutional Networks (GCNs) have shown tremendous success in
recommender systems and collaborative filtering (CF), the mechanism of how they …

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …

The remarkable progress of network embedding has led to state-of-the-art algorithms in
recommendation. However, the sparsity of user-item interactions (ie, explicit preferences) on …

Personalized recommendation plays an important role in many online services. Substantial
research has been dedicated to learning embeddings of users and items to predict a user's …

Graph convolution networks (GCNs), with their efficient ability to capture high-order
connectivity in graphs, have been widely applied in recommender systems. Stacking …

Graph Convolutional Networks (GCNs) are powerful for collaborative filtering. The key
component of GCNs is to explore neighborhood aggregation mechanisms to extract high …