34 citations · 38 across the 4 of their papers we have counts for
4 papers
Informative Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
The quality of graph-structured data is fundamental to the success of modern graph analysis techniques such as Graph Neural Networks (GNNs). However, real-world graph data is often…
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Sirui Chen, Shen Han, Jiawei Chen +6
Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of R…
Uncertainty-Aware Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
Graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph…
SIGformer: Sign-aware Graph Transformer for Recommendation
Sirui Chen, Jiawei Chen, Sheng Zhou +5
In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback…