1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective
Binbin Hu, Weifan Wang, Hanshu Wang +4
Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative sou…