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 …
A survey of graph neural network based recommendation in social networks
X Li, L Sun, M Ling, Y Peng - Neurocomputing, 2023 - Elsevier
With the widespread popularization of social network platforms, user-generated content and
other social network data are growing rapidly. It is difficult for social users to select interested …
other social network data are growing rapidly. It is difficult for social users to select interested …
A systematic literature review of mobile application usability: addressing the design perspective
Z Huang, M Benyoucef - Universal Access in the Information Society, 2023 - Springer
Advances in mobile technologies and wireless Internet services have accelerated the
growth of the mobile app market. To nurture such growth, the usability of mobile apps must …
growth of the mobile app market. To nurture such growth, the usability of mobile apps must …
An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information
In recent years, explainable recommendation has been a topic of active study. This is
because the branch of the machine learning field related to methodologies is enabling …
because the branch of the machine learning field related to methodologies is enabling …
Neighbor enhanced graph convolutional networks for node classification and recommendation
Abstract The recently proposed Graph Convolutional Networks (GCNs) have achieved
significantly superior performance on various graph-related tasks, such as node …
significantly superior performance on various graph-related tasks, such as node …
Alex: Towards effective graph transfer learning with noisy labels
Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …
DCOM-GNN: A deep clustering optimization method for graph neural networks
H Yang, J Wang, R Duan, C Yan - Knowledge-Based Systems, 2023 - Elsevier
Deep clustering plays an important role in data analysis, and with the prevalence of graph
data nowadays, various deep clustering models on graph are constantly proposed …
data nowadays, various deep clustering models on graph are constantly proposed …
Osgnn: Original graph and subgraph aggregated graph neural network
Abstract Heterogeneous Graph Embedding (HGE) is receiving a great attention from
researchers, as it can be widely and effectively used to solve problems from various real …
researchers, as it can be widely and effectively used to solve problems from various real …