arXiv AI June 2, 2026 By Yangchen Zeng, Jinze Wang

Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation

arXiv:2604. 20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization.

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arXiv AI

Sep 25

From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation

The paper proposes a retrieval‑grounded credit‑assignment method for generative recommenders that use Semantic IDs (SIDs). By structuring each generated trace into a history summary, a set of interest hypotheses, and a final SID, and then verifying each hypothesis with a frozen retriever, the method assigns reward at the hypothesis level rather than only at the final SID. Experiments on Amazon Reviews datasets show consistent improvements in SID recommendation, and an oracle analysis on Video Games data demonstrates that selecting target‑relevant queries among generated interests boosts recall and ranking.

By Mengdan Zhu, Yufan Zhao, Yao Zhao, Sophie Di, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao

Hugging Face Trending Papers

Sep 24

From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation

The paper introduces a retrieval‑grounded credit‑assignment method for generative recommenders that use Semantic IDs (SIDs). By structuring each autoregressive trace into a history summary, a set of interest hypotheses, and a final SID, a frozen retriever verifies each hypothesis as a catalog query. Rewards are assigned at the hypothesis level when any query retrieves the target within the top‑K, allowing distinct updates for rollouts that share the same SID reward and improving SID recommendation performance on Amazon Reviews datasets.

arXiv AI

Sep 1

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...

By Tianxin Wei, Xuying Ning, Xuxing Chen, Ruizhong Qiu, Yupeng Hou, Yan Xie, Shuang Yang, Zhigang Hua, Jingrui He

llms rag diffusion nlp efficiency benchmarks

arXiv AI

Aug 18

Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

arXiv:2608. 11980v2 Announce Type: replace-cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.

By Kangning Zhang, Haotian Fang, Xukun Luo, Hao Yin, Yang Gao, Peng Yan, Weiwen Liu, Weinan Zhang, Yong Yu

reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 29

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.

By Shutong Qiao, Wei Yuan, Tong Chen, Hao Wang, Quoc Viet Hung Nguyen, Hongzhi Yin

llms efficiency

arXiv Machine Learning

Sep 22

Guiding the coarse levels of semantic IDs makes the fine levels learnable

arXiv:2609.22227v1 Announce Type: cross Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...

By Bin Wang, Zhengyu Zhang

llms rag nlp reinforcement-learning safety

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Related stories

From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation

From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

Guiding the coarse levels of semantic IDs makes the fine levels learnable