3 papers
cs.LG2026
RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation
Yixuan Huang, Jiawei Chen, Shengfan Zhang +1
Collaborative filtering (CF) recommendation has been significantly advanced by integrating Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). However, (i) random ed…
cs.IR2024
Position-aware Graph Transformer for Recommendation
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have adva…
cs.LG2024
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
Binbin Hu, Zhicheng An, Zhengwei Wu +6
Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influen…