214 citations · 1k across the 16 of their papers we have counts for
16 papers
Fairness of ChatGPT
Yunqi Li, Lanjing Zhang, Yongfeng Zhang
Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment. However, there is a limited number of quantitative analyses and in-depth studies regardin…
Transferable Fairness for Cold-Start Recommendation
Yunqi Li, Dingxian Wang, Hanxiong Chen +1
With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem. Previous works on fairness-…
Causal Inference for Recommendation: Foundations, Methods and Applications
Shuyuan Xu, Jianchao Ji, Yunqi Li +3
Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recomme…
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
Hanxiong Chen, Yunqi Li, He Zhu +1
Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems.…
A Survey on Trustworthy Recommender Systems
Yingqiang Ge, Shuchang Liu, Zuohui Fu +6
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. How…
Fairness in Recommendation: Foundations, Methods and Applications
Yunqi Li, Hanxiong Chen, Shuyuan Xu +4
As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and t…
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…
Graph Collaborative Reasoning
Hanxiong Chen, Yunqi Li, Shaoyun Shi +3
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. Ho…
Counterfactual Explainable Recommendation
Juntao Tan, Shuyuan Xu, Yingqiang Ge +3
By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem.…
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…
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu +2
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects…
Causal Collaborative Filtering
Shuyuan Xu, Yingqiang Ge, Yunqi Li +3
Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlat…
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 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…
Neural Collaborative Reasoning
Hanxiong Chen, Shaoyun Shi, Yunqi Li +1
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models,…