101 citations · 128 across the 4 of their papers we have counts for
6 papers
Causal Structure Learning with Recommendation System
Shuyuan Xu, Da Xu, Evren Korpeoglu +4
A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by us…
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
Juntao Tan, Shijie Geng, Zuohui Fu +4
Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the comp…
Counterfactual Evaluation for Explainable AI
Yingqiang Ge, Shuchang Liu, Zelong Li +6
While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind th…
Personalized Counterfactual Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Shuyuan Xu +2
Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore…
Efficient Non-Sampling Knowledge Graph Embedding
Zelong Li, Jianchao Ji, Zuohui Fu +4
Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negat…
Learning Personalized Risk Preferences for Recommendation
Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3
The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…