6 papers
Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
Zhirong Huang, Debo Cheng, Guixian Zhang +3
Instrumental variables (IVs) are crucial for addressing unobservable confounders, yet their stringent exogeneity assumptions pose significant challenges in networked data. Existing…
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
Chengyu Li, Debo Cheng, Guixian Zhang +2
Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predic…
Towards Fair Graph Representation Learning in Social Networks
Guixian Zhang, Guan Yuan, Debo Cheng +3
With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim t…
Multi-Cause Deconfounding for Recommender Systems with Latent Confounders
Zhirong Huang, Shichao Zhang, Debo Cheng +3
In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in d…
Mitigating Propensity Bias of Large Language Models for Recommender Systems
Guixian Zhang, Guan Yuan, Debo Cheng +3
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analy…
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems
Zhirong Huang, Shichao Zhang, Debo Cheng +3
In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in…