197 citations · 240 across the 5 of their papers we have counts for
6 papers
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…
Variation Control and Evaluation for Generative SlateRecommendations
Shuchang Liu, Fei Sun, Yingqiang Ge +2
Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations. In o…
Discrete Knowledge Graph Embedding based on Discrete Optimization
Yunqi Li, Shuyuan Xu, Bo Liu +4
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable di…
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8
As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior appro…
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…
Learning Post-Hoc Causal Explanations for Recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu +3
State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of blac…