A survey of graph neural networks for recommender systems: Challenges, methods, and directions
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 …
Recently, graph neural networks have become the new state-of-the-art approach to …
A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Self-supervised heterogeneous graph neural network with co-contrastive learning
Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown
superior capacity of dealing with heterogeneous information network (HIN). However, most …
superior capacity of dealing with heterogeneous information network (HIN). However, most …
Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but
the unique data processing and evaluation setups used by each work obstruct a full …
the unique data processing and evaluation setups used by each work obstruct a full …
A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
Contrastive meta learning with behavior multiplicity for recommendation
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 …
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …
Interest-aware message-passing GCN for recommendation
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is
attributed to their capability on learning good user and item embeddings by exploiting the …
attributed to their capability on learning good user and item embeddings by exploiting the …
Heterogeneous graph structure learning for graph neural networks
Abstract Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention
in recent years and achieved outstanding performance in many tasks. The success of the …
in recent years and achieved outstanding performance in many tasks. The success of the …
Debiasing graph neural networks via learning disentangled causal substructure
Abstract Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by
learning the correlation between the input graphs and labels. However, by presenting a …
learning the correlation between the input graphs and labels. However, by presenting a …