Graph convolutional network (GCN)‐based recommendation has recently attracted
significant attention in the recommender system community. Although current studies …

Collaborative filtering-based methods have achieved distinctive performance in ordinary
recommendation tasks. However, they suffer from a cold-start problem when historical …

In recent years, review-based methods have been widely used to learn user representations
because reviews contain abundant information. However, few users would like to write …

[Objective] This paper reviewed the latest achievements of recommendation systems based
on the knowledge graph.[Coverage] We used “knowledge graph”,“KG”,“recommendation …

Graph convolutional networks (GCNs) can quickly and accurately learn graph
representations and have shown powerful performance in many graph learning domains …

Due to the complementary nature of graph neural networks and structured data in
recommendations, recommendation systems using graph neural network techniques have …

Graph Convolutional Neural Networks (GNN) based recommender systems are state-of-the-
art since they can capture the high order collaborative signals between users and items …

Knowledge graphs as external information has become one of the mainstream directions of
current recommendation systems. Various knowledge-graph-representation methods have …

Variational autoencoder (VAE) is known as a classic and effective method in modeling
users' homogeneous behaviors in recommender systems. In recent years, graph neural …

Learning informative representations (aka. embeddings) of users and items is the core of
modern recommender systems. Previous works exploit user-item relationships of one-hop …