MCCL combines an attention-denoised relational graph network and a graph variational autoencoder through contrastive learning, claiming up to 0.8% RMSE and up to 36% ranking metric improvements on Amazon datasets.
Lightgcl: Simple yet effective graph contrastive learning for recommendation
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Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
MCCL combines an attention-denoised relational graph network and a graph variational autoencoder through contrastive learning, claiming up to 0.8% RMSE and up to 36% ranking metric improvements on Amazon datasets.