To address these problems, graph contrastive learning is applied for GCN-based recommendation. The general framework of graph contrastive learning is first to perform data augmentation on the input graph to get two graph views and then maximize the agreement of representations in these views.
Oct 27, 2020 · In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological ...
Mar 17, 2023 · In this work, we propose a novel Graph Contrastive learning framework with Adaptive data augmentation for Recommendation (GCARec).
Jun 3, 2021 · In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological ...
In this paper, we propose Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation (GCARec).
This work proposes a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators.
Oct 27, 2020 · GCA consistently outperforms existing methods and our unsuper- vised method even surpasses its supervised counterparts on several transductive ...
Missing: Recommendation. | Show results with:Recommendation.
A novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators.
Graph Convolutional Network (GCN) has been one of the most popular technologies in recommender systems, as it can effectively model high-order relationships ...
This is the code for the WWW 2021 Paper: Graph Contrastive Learning with Adaptive Augmentation. Usage For example, to run GCA-Degree under WikiCS, execute:
Missing: Recommendation. | Show results with:Recommendation.