Search Results for author:
Found 4 papers, 4 papers with code
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings.
Bias and Debias in Recommender System: A Survey and Future Directions
This motivates us to provide a systematic survey of existing work on RS biases.
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.
AutoDebias: Learning to Debias for Recommendation
This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
