To address these challenges, we propose Hierarchical Graph Contrastive Learning (HGCL) for advancing review-enhanced recommendation systems. HGCL dynamically learns hypergraph structures to capture higher-order correlations among nodes and simultaneously integrates local and global collaborative relations through...
但是,这种历史评论的使用过于粗粒度,无法充分利用评论信息。因此,需要开发对评论的细粒度利用,以在用户-项目交互建模阶段获取详细信息。为了区分历史评论的使用,作者将方法2)称为目标评论的使用,因为每次评论都为相应的用户-项目交互建模提供详细描述,以预测目标评级。 尽管上述两种方案都取得了很大的进展,但作者认为,目...
(1)图的自动编码 (Graph auto-encoders),可以用于节点聚类和链路预测的场景中 (2)对比学习(Contrastive learning),通过图级表示和不同级别的节点结构的互信息最大化来进行学习图信息 GNN的分析 (1)图信号处理 最早处理图卷积的是拉普拉斯平滑,对特征矩阵进行平滑,能够提取到附近节点的特征。拉普拉斯平滑的理论基础是...
GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023) graph-contrastive-learningheterophily-graph UpdatedJun 11, 2024 Python [SIGIR 2022] A Review-aware Graph Contrastive Learning Framework for Recommendation recommender-systemgraph-contrastive-learningreview-based-recommendation ...
In this work, we propose a novel graph contrastive learning framework, named Accurate Difference-based Node-Level Graph Contrastive Learning (DNGCL), which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs. Specifically, we train the ...
Graph contrastive learning has been widely used in recommender systems to extract meaningful representations by analyzing the similarities and differences between data samples. However, existing methods often suffer from complex architectures, inefficient representation learning, and lack of attention to the ...
graph auto-encoders, contrastive learning.graph auto-encodersGAE (graph auto-encoders), VGAE (variational GAE) (Kipf and Welling, 2016), It uses a simple decoder to reconstruct the adjacency matrix.H=GCN(X,A)~A=f(HHT)H=GCN(X,A)A~=f(HHT)...
which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomi...
et al. Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration. Health Inf Sci Syst 12, 12 (2024). https://doi.org/10.1007/s13755-024-00274-x Download citation Received28 May 2023 Accepted09 January 2024 Published23 February 2024 DOIhttps://...
Contrastive learning is a commonly used framework in the field of graph self-supervised learning, where models are trained by bringing positive samples closer together and pushing negative samples apart. Most existing graph contrastive learning models divide all nodes into positive and negative samples,...