2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world…
cs.IR2024★ 2 cited
Mixed Supervised Graph Contrastive Learning for Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from h…
cs.CL2023★ 1 cited
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
Henry Peng Zou, Yue Zhou, Weizhi Zhang +1
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organi…