24 citations · 62 across the 35 of their papers we have counts for
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cs.IR2025
When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation
Zongwei Wang, Min Gao, Junliang Yu +3
Graph Contrastive Learning (GCL) has demonstrated substantial promise in enhancing the robustness and generalization of recommender systems, particularly by enabling models to leve…
cs.IR2025★ 1 cited
How Do Experts Make Sense of Integrated Process Models?
Tianwa Chen, Barbara Weber, Graeme Shanks +3
A range of integrated modeling approaches have been developed to enable a holistic representation of business process logic together with all relevant business rules. These approac…
cs.IR2025
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents
Zongwei Wang, Min Gao, Junliang Yu +3
The implicit feedback (e.g., clicks) in real-world recommender systems is often prone to severe noise caused by unintentional interactions, such as misclicks or curiosity-driven be…