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Found 4 papers, 4 papers with code
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.
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.
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.
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.
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