Author

Shou-Jin Wang

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Preprint Oct 2026

Generate What You Can Trust: Content Credibility in Generative Recommenders

CreGR is the first credible GR model that jointly tackles content credibility across the two core stages of GR: tokenization and generation, and introduces an asymmetric masking probability reduction strategy that selectively diminishes the contribution of tokens associated with uncredible content to the generation pro...

Zhuo Cai, Guang-Hao Wu, Shou-Jin Wang et al. · 0 citations

Sep 2026

SPRINT: Single-Step Generative Recommendation via Average Probability Velocity

Semantic ID (SID) based generative recommendation represents each item as a sequence of discrete tokens, and recommends by generating the SID of the item a user would like to interact with. Both dominant paradigms in this domain generally pay for generation token by token: autoregressive models decode the tokens left-t...

Zhuo Cai, Shou-Jin Wang, Pei-Lin Zhou et al. · arXiv.org · 3 citations

Book Open access Sep 2026

DPGFlow: Decoupled Preference Guided Flow Matching for Cross Domain Sequential Recommendation

DPGFlow is proposed, the first Flow Matching framework specifically designed for CDSR, which enables stable and efficient few-step inference and facilitates effective knowledge transfer under heterogeneous and noisy behaviors.

Xiao-Xin Ye, Chengkai Huang, Hong-Tao Huang et al. · Proceedings of the 20th ACM... · 0 citations