Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Temporal and heterogeneous graph neural network for financial time series prediction
The price movement prediction of stock market has been a classical yet challenging
problem, with the attention of both economists and computer scientists. In recent years …
problem, with the attention of both economists and computer scientists. In recent years …
Multi-behavior graph neural networks for recommender system
Recommender systems have been demonstrated to be effective to meet user's personalized
interests for many online services (eg, E-commerce and online advertising platforms) …
interests for many online services (eg, E-commerce and online advertising platforms) …
Unsupervised structure-adaptive graph contrastive learning
Graph contrastive learning, which to date has always been guided by node features and
fixed-intrinsic structures, has become a prominent technique for unsupervised graph …
fixed-intrinsic structures, has become a prominent technique for unsupervised graph …
Investigating out-of-distribution generalization of GNNs: An architecture perspective
Graph neural networks (GNNs) have exhibited remarkable performance under the
assumption that test data comes from the same distribution of training data. However, in real …
assumption that test data comes from the same distribution of training data. However, in real …
Personalized complementary product recommendation
Complementary product recommendation aims at providing product suggestions that are
often bought together to serve a joint demand. Existing work mainly focuses on modeling …
often bought together to serve a joint demand. Existing work mainly focuses on modeling …
Efficient complementary graph convolutional network without negative sampling for item recommendation
B Wu, L Zhong, H Li, Y Ye - Knowledge-Based Systems, 2022 - Elsevier
Learning vector representations (aka, embeddings) of users and items lies at the heart of
building a modern recommender system. Typically, graph neural networks (GNN) have been …
building a modern recommender system. Typically, graph neural networks (GNN) have been …
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 …
Enhanced multi-relationships integration graph convolutional network for inferring substitutable and complementary items
H Chen, J He, W Xu, T Feng, M Liu, T Song… - Proceedings of the …, 2023 - ojs.aaai.org
Understanding the relationships between items can improve the accuracy and
interpretability of recommender systems. Among these relationships, the substitute and …
interpretability of recommender systems. Among these relationships, the substitute and …