3 papers
cs.IR2026
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…
cs.IR2026
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…
cs.IR2025
Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods
Meng Yan, Cai Xu, Xujing Wang +3
Recommender systems based on graph neural networks perform well in tasks such as rating and ranking. However, in real-world recommendation scenarios, noise such as user misuse and…