16 citations · 30 across the 5 of their papers we have counts for
7 papers
A Comprehensive Survey on Trustworthy Recommender Systems
Wenqi Fan, Xiangyu Zhao, Xiao Chen +8
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalize…
Trustworthy AI: A Computational Perspective
Haochen Liu, Yiqi Wang, Wenqi Fan +6
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human soci…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
Does Gender Matter? Towards Fairness in Dialogue Systems
Haochen Liu, Jamell Dacon, Wenqi Fan +3
Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recog…
Deep Social Collaborative Filtering
Wenqi Fan, Yao Ma, Dawei Yin +3
Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via t…
Deep Adversarial Social Recommendation
Wenqi Fan, Tyler Derr, Yao Ma +3
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social netwo…