21 papers
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
Ziyu Zheng, Zhengshun Du, Yaming Yang +5
The paper proposes TopoGR, a generative recommendation framework that uses binary semantic IDs with explicit Hamming geometry to preserve the latent topology of item representation…
EviRank: Evidence-Based Confidence Estimation for LLM-Based Ranking
Meng Yan, Cai Xv, Xujing Wang +2
Large Language Models show promise for recommendation, but they raise reliability concerns due to limited domain coverage and inherent stochasticity. Existing uncertainty quantific…
Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification
Yilin Zhang, Cai Xu, You Wu +2
Real-world model deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unreliable, posing severe risks in safety-c…
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
Ziyu Zheng, Yaming Yang, Ziyu Guan +2
Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly r…
Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment
Ziyu Zheng, Yaming Yang, Zhe Wang +2
While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHGs) remains a formidable challe…
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
Yilin Zhang, Cai Xu, Haishun Chen +2
Trusted multi-view classification typically relies on a view-wise evidential fusion process: each view independently produces class evidence and uncertainty, and the final predicti…