3 papers
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…
cs.IR2024
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…