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cs.IR2026
From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
Mengdan Zhu, Yufan Zhao, Yao Zhao +5
Semantic IDs (SIDs) encode each catalog item as a short token sequence, enabling generative recommenders to predict the next item autoregressively. Reasoning-enhanced variants, an…
cs.IR2026
Learning Better Reasoning for Generative Recommendation with Semantic IDs
Mengdan Zhu, Yufan Zhao, Sophie Di +5
Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user's interaction history. Semanti…
cs.CL2026
Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation
Mengdan Zhu, Yufan Zhao, Tao Di +2
News recommendation plays a critical role in online news platforms by helping users discover relevant content. Cross-domain news recommendation further requires inferring user's un…