A survey of graph neural networks for social recommender systems

K Sharma, YC Lee, S Nambi, A Salian, S Shah… - ACM Computing …, 2024 - dl.acm.org
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 …

Enhancing explainable rating prediction through annotated macro concepts

H Zhou, S Zhou, H Chen, N Liu, F Yang… - Proceedings of the …, 2024 - aclanthology.org
Generating recommendation reasons for recommendation results is a long-standing
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

M Heidari, L Ding, M Kheshti, W Bao, X Zhao… - International Journal of …, 2024 - Elsevier
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 …

A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation

H Ye, Y Song, M Li, F Cao - Information Processing & Management, 2023 - Elsevier
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 …

based Multi-intention Contrastive Learning for Recommendation

W Yang, T Huo, Z Liu, C Lu - Proceedings of the 46th International ACM …, 2023 - dl.acm.org
Real recommendation systems contain various features, which are often high-dimensional,
sparse, and difficult to learn effectively. In addition to numerical features, user reviews …

Adaptive-propagating heterophilous graph convolutional network

Y Huang, Y Shi, Y Pi, J Li, S Wang, W Guo - Knowledge-Based Systems, 2024 - Elsevier
Graph convolutional networks have significant advantages in dealing with graph-structured
data, but most existing methods usually potentially assume that nodes belonging to the …

Towards adversarially robust recommendation from adaptive fraudster detection

Y Lai, Y Zhu, W Fan, X Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The robustness of recommender systems under node injection attacks has garnered
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

B Yu, H Xie, Y Fu, Z Xu - Applied Soft Computing, 2024 - Elsevier
Abstract The Graph Convolutional Neural Networks (GCNs) have demonstrated a powerful
capacity for relation processing. To improve the representation learning capability of GCNs …

Efficient Unsupervised Graph Embedding with Attributed Graph Reduction and Dual-level Loss

Z Liu, C Wang, H Feng, Z Chen - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Graph embedding aims to extract low-dimensional representation vectors, commonly
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 …