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 …
Enhancing explainable rating prediction through annotated macro concepts
Generating recommendation reasons for recommendation results is a long-standing
problem because it is challenging to explain the underlying reasons for recommending an …
problem because it is challenging to explain the underlying reasons for recommending an …
[HTML][HTML] A review on application of machine learning-based methods for power system inertia monitoring
The modernization of electrical power systems is reflected through the integration of
renewable energy resources, with the ultimate aim of creating a carbon–neutral world …
renewable energy resources, with the ultimate aim of creating a carbon–neutral world …
A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation
Graph neural networks have been frequently applied in recommender systems due to their
powerful representation abilities for irregular data. However, these methods still suffer from …
powerful representation abilities for irregular data. However, these methods still suffer from …
based Multi-intention Contrastive Learning for Recommendation
Real recommendation systems contain various features, which are often high-dimensional,
sparse, and difficult to learn effectively. In addition to numerical features, user reviews …
sparse, and difficult to learn effectively. In addition to numerical features, user reviews …
Adaptive-propagating heterophilous graph convolutional network
Graph convolutional networks have significant advantages in dealing with graph-structured
data, but most existing methods usually potentially assume that nodes belonging to the …
data, but most existing methods usually potentially assume that nodes belonging to the …
Towards adversarially robust recommendation from adaptive fraudster detection
The robustness of recommender systems under node injection attacks has garnered
significant attention. Recently, GraphRfi, a Graph-Neural-Network-based (GNN-based) …
significant attention. Recently, GraphRfi, a Graph-Neural-Network-based (GNN-based) …
Three-way graph convolutional network for multi-label classification in multi-label information system
Abstract The Graph Convolutional Neural Networks (GCNs) have demonstrated a powerful
capacity for relation processing. To improve the representation learning capability of GCNs …
capacity for relation processing. To improve the representation learning capability of GCNs …
Efficient Unsupervised Graph Embedding with Attributed Graph Reduction and Dual-level Loss
Graph embedding aims to extract low-dimensional representation vectors, commonly
referred to as embeddings, from graph data. The generated embeddings simplify …
referred to as embeddings, from graph data. The generated embeddings simplify …
Inhomogeneous Interest Modeling via Hypergraph Convolutional Networks for Social Recommendation
L Luo, M Wang, J Liu, J Huang - 2024 International Joint …, 2024 - ieeexplore.ieee.org
To improve the performance of social recommendation, it can be beneficial to include social
relationships, model user interests, and item attraction. Traditional methods use binary …
relationships, model user interests, and item attraction. Traditional methods use binary …