Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence
Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …
powerful modeling capabilities and have been successfully applied in natural language …
A survey of graph neural networks for social recommender systems
Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …
interactions as well as the user-to-user social relations for the task of generating item …
GNN-based long and short term preference modeling for next-location prediction
Next-location prediction is a special task of the next POIs recommendation. Different from
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
High-order graph attention network
GCN is a widely-used representation learning method for capturing hidden features in graph
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …
DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems
In recent years, with the development of Internet technology, recommender systems have
been widely used by virtue of their ability to meet the personalized needs of users. In order …
been widely used by virtue of their ability to meet the personalized needs of users. In order …
A survey on cross-media search based on user intention understanding in social networks
With the increasing popularity of online social networks, more and more people are posting
information, updating their statuses, and searching for topics there. Massive cross-media big …
information, updating their statuses, and searching for topics there. Massive cross-media big …
BAR: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering
In our daily life, we are often greatly assisted with recommendation engines in finding the
required information efficiently and accurately. In this paper, we focus on an emerging and …
required information efficiently and accurately. In this paper, we focus on an emerging and …
Adversarial camouflage for node injection attack on graphs
Node injection attacks on Graph Neural Networks (GNNs) have received increasing
attention recently, due to their ability to degrade GNN performance with high attack success …
attention recently, due to their ability to degrade GNN performance with high attack success …
Exploiting node-feature bipartite graph in graph convolutional networks
Abstract In recent years, Graph Convolutional Networks (GCNs), which extend convolutional
neural networks to graph structure, have achieved great success on many graph learning …
neural networks to graph structure, have achieved great success on many graph learning …
A multi-behavior recommendation method exploring the preference differences among various behaviors
M Gan, G Xu, Y Ma - Expert Systems with Applications, 2023 - Elsevier
User behavior data has been widely used in recent research of recommendation systems.
Existing work usually utilize only single behavior instead of multi-behavior. However, there …
Existing work usually utilize only single behavior instead of multi-behavior. However, there …