Author

Pei-Lin Zhou

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Preprint Oct 2026

Generate What You Can Trust: Content Credibility in Generative Recommenders

Generative recommendation (GR) represents items with semantic IDs (i.e., discrete token sequences) and generates target item tokens as recommendations. Despite its promising results, existing methods predominantly optimize for accuracy while neglecting the credibility of the recommendations they generate. This oversigh...

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

Preprint 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. · 1 citation