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cs.IR2026
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
Ziyu Zheng, Zhengshun Du, Yaming Yang +5
Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs. However, existing me…
cs.AI2026
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jie Peng, Yanping Zheng, Zhewei Zhe +3
Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world re…
cs.LG2026
Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
Haoran Zhang, Chuanpu Li, Yuxin Fu +4
Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and de…